Keywords: Distributed cooperative control over networks
Abstract: Optimization is a building block for the solution of several problems in estimation, learning, decision and control. Nowadays, due to the massive and ubiquitous presence of smart devices with sensing and control capabilities, large-scale optimization problems arise in these areas, so that powerful computing architectures are required to solve them. On the other side, smart networks of connected devices can be seen as extremely powerful, but completely unstructured, “supercomputing” infrastructures, in which nodes have only a partial knowledge of the optimization problem to solve. Distributed optimization aims at exploiting such an opportunity by developing novel methods, based on local computation and communication, allowing network processors to cooperatively solve the global optimization problem without relying on any central unit. In this talk I will start introducing some classes of structured optimization problems motivated by learning and control applications in cyber-physical networks, and show key challenges that arise in a distributed computation framework. Then, I will present novel distributed optimization methods to address some of these challenges as: asynchronous and possibly unreliable communication, a large number of decision variables and/or constraints, and nonconvex problems as, e.g., mixed-integer programs.

Keywords: Adaptive control, Aerospace, Nonlinear system theory
Abstract: This paper addresses the six-degree-of-freedom adaptive control problem for final phase proximity operations of spacecraft autonomous rendezvous and docking in the presence of parameter uncertainties. The salient feature of this work is that motion constraints during the proximity phase are considered in the proposed adaptive control formulation. Dual quaternions are employed to describe the coupled rotation and translation model of spacecraft, and an artificial potential fucntion is designed to encode information regarding motion constraints. In order to account for mass and inertia uncertainties, a non-certainty-equivalence adaptive controller is presented under the dual-quaternion formalism, which guarantees the arrival of the follower spacecraft at the docking port of the leader with a prescribed relative pose, while accommodating for parameter uncertianties and complying with underlying motion constraints. Simulation results are demonstrated to show the various features of the proposed adaptive control design.

Keywords: Adaptive control, Delay systems, Modeling
Abstract: Inspired by humans' ability to adapt to changing environments, this paper proposes an adaptive human model that mimics the crossover model despite input bandwidth deviations and plant uncertainties. The proposed human pilot model structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stability criteria applied to the overall closed loop system including the human pilot and the plant. The proposed model can be employed for human-in-the-loop stability and performance analyses with different controllers and plant types. A numerical example is used to demonstrate the effectiveness of the presented method.

Keywords: Adaptive control, Energy systems, Process control
Abstract: This paper discusses the joint use of control configuration selection and adaptive model predictive control for the reconfiguration and improvement of a control system for the heat generation and distribution in a district heating system. The traditional PID-type multi-loop controller configuration is assessed from its feasibility and potential to be improved, on the basis of automatically generated models. The studied test case is the district heating system in Lulea, Sweden.

It is found that a multivariable controller for a subsystem of the complete system could largely improve the performance of the system and thereby addressing confirmed oscillations in the system. An adaptive model predictive controller is implemented in the realistic simulator OPTi-Sim for the district heating system in Lulea. Simulation results confirm that the oscillations which are present in the simulation with the current control scheme can be largely reduced through the use of the proposed controlled.

Keywords: Adaptive control, Sampled data control, Linear systems
Abstract: The paper addresses the problem of performance improvement of direct model reference adaptive control (MRAC) of discrete linear time-invariant (LTI) plants. Two new solutions to the problem are proposed and use the idea of regressor recording together with the principle of augmented error. The recording is provided by means of special filters (linear operators with “memory”) and permits to accelerate the tuning of adjustable controller and, hence accelerate the transients in the closed-loop system. The first solution is based on dynamical regression extension (DRE) of augmented error invoking its multiple filtering. The second solution is based on memory regressor extension (MRE) of time-varying state matrix omegaomega^top (omega is the vector of regressor) of parametric error model by applying one SISO filter. It is shown that adaptation algorithms motivated by DRE and MRE procedures offer potentially fast parametric convergence in comparison with standard gradient-based adaptation algorithm. Moreover, it is proved and demonstrated by examples that new adaptation algorithms provide asymptotic (not exponential) convergence not under persistent excitation (PE) condition, but under weaker, so called, not-in-L1, condition. Theoretical results are illustrated via comparable simulation.

Keywords: Adaptive control, Supervisory control, Linear parameter-varying systems
Abstract: This paper develops a multiple fixed model blending based adaptive parameter identification scheme for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification scheme is proven to be asymptotically stable for uncertain linear time-invariant MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification scheme, a linear-quadratic (LQ) optimal multiple model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability and effectiveness of the proposed MMAC scheme.

Keywords: Adaptive systems, Lyapunov methods, Stability of nonlinear systems
Abstract: The problem of adaptive estimation of constant parameters in the linear regressor model is studied without the hypothesis that regressor is Persistently Excited (PE). First, the initial vector estimation problem is transformed to a series of the scalar ones using the method of Dynamic Regressor Extension and Mixing (DREM). Second, several adaptive estimation algorithms are proposed for the scalar scenario. In such a case, if the regressor may be nullified asymptotically or in a finite time, then the problem of estimation is also posed on a finite interval of time. The efficiency of the proposed algorithms is demonstrated in numeric experiments for an academic example

Keywords: Control over communication, Optimal control of communication networks, Constrained control
Abstract: This paper proposes a novel online scheduling approach for perturbed Networked Control Systems (NCS) over lossy networks, subject to state and input constraints and limited network capacity.

The paper shows how an online communication schedule can be found for the considered class of systems, as the solution of a constrained optimization problem, which is solved in receding horizon and is proven to be persistently feasible. The resulting online schedule is proven to preserve the system invariance w.r.t. state and input constraints. A communication scheduler is also derived for lossy networks, starting from a feasible baseline schedule calculated offline.

Numerical examples are given to illustrate the proposed contributions.

Keywords: Control over networks, Control over communication, Optimal control
Abstract: We consider the resource allocation problem for a system consisting of multiple control subsystems that share an unknown unreliable time-varying wireless channel. We propose a novel method for granting channel access deterministically without requiring information exchange between subsystems, based on local timers for prioritizing channel access with respect to a local cost that takes into account the (initially unknown) channel conditions. We propose a novel setup which has the capability of learning the parameters of imperfect communication links while ensuring distributed collision-free implementation. More specifically, we adopt learning algorithms for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. The efficacy of this mechanism is evaluated via simulations.

Keywords: Control over networks, Control over communication, Distributed control
Abstract: In this paper, we consider a system consisting of multiple (possibly heterogeneous) decoupled control subsystems which aim at communicating with their corresponding controllers via shared (possibly) time-varying wireless channels. To address the resource allocation problem in a distributed fashion, we propose a timer-based channel access mechanism in which the subsystem with the smallest timer value, in a channel, claims the slot for transmission in that specific channel. The value of the timer is inversely proportional to a cost which is a function of the temporal correlation in the channel variation and the subsystem state. This cost can be calculated individually and does not require explicit communication between the subsystems, since it is based on locally available information only. The temporal correlation in the channel variation may be unknown and, in such cases, each subsystem tries to deduce it via machine learning techniques. The performance of our proposed mechanism is demonstrated via simulations.

Keywords: Control over networks, Control over communication, Robotics
Abstract: Wireless communication is a very appealing technology for industrial automation and networked control systems but much more unreliable as compared to wired solutions. Existing wireless standards for industrial automation such as ISA100 and WirelessHart address such unreliability but are limited to low control rates not exceeding 20 Hz, which are suitable for supervisory tasks, but not for high-bandwidth applications. In this work, we propose to use Wi-Fi, which thanks to its high data rates (>300 Mbit/s), seems to be eligible for high performance applications requiring control rates greater than 1 kHz. Preliminary works mostly rely on emulation approaches, in which no change in the control design is needed, but the communication protocols have to be modified to enforce determinism (e.g. via TDMA-like protocols) at the price of longer delays. Conversely, this work follows a different paradigm, where the unreliability of the communication, due to random packet losses and delays, is coped by a robust model-based controller, which is capable of dealing with the stochastic nature of the communication channel while guaranteeing high control rates. The proposed approach has been tested on a hardware-in-the-loop laboratory setup involving conventional off-the-shelf Wi-Fi network interface cards, which showed how control rates in the order of 1 kHz are well within the possibility of Wi-Fi, even in the presence of substantial communication channel noise.

Keywords: Control over networks, Lyapunov methods, Stability of linear systems
Abstract: We address the problem of local synchronization of two DC/AC converters connected via a RL line in closed-loop with machine matching control by pursuing a novel approach for the assessment of small-signal stability. Our method is defined using an oblique projection. We use this Lyapunov function to obtain a local stability certificate in the neighborhood of a well-defined set of equilibria characterized by frequency synchronization. To this end, we first reduce the fifth-order DC/AC converter model to a third-order one. Then, we investigate local stability based on Lyapunov's method performed on the linearized and reduced-order model. Moreover, we certify stability via sufficient and fully decentralized conditions that can be satisfied by choosing an appropriate operating point and control gains. Finally, we demonstrate our results using numerical simulations.

Keywords: Control over networks, Stochastic systems, Linear systems
Abstract: In the present paper, we extend the time-delay approach to networked stochastic systems with state multiplicative noise. Two classes of protocols are considered. The first one is defined by Round-Robin (RR) protocol where the sampled measurements are sent in a periodic manner one after another, whereas the second is defined by try-once-discard (TOD) protocol where the sampled measurements sent have the greatest weighted error. The resulting closed-loop systems are modeled as a system with multiple time-varying delays and a hybrid system with a time-varying delay in the dynamics and in the reset conditions, respectively. By use of Lyapunov-Krasovskii approach, sufficient conditions for guaranteeing the exponential mean square stability are obtained in terms of linear matrix inequalities (LMIs). An example of batch reactor illustrates the efficiency of the results.

Keywords: Cooperative control, Decentralized control, Traffic control
Abstract: This paper describes a decentralized control strategy for the automation of road intersections and studies its impact on traffic in a realistic urban road network. The controller incorporates a consensus-based auction algorithm (CBAA-M), which allows vehicles to agree on a crossing order at each road intersection, and an on-board model predictive controller that avoids collisions with other traffic participants, while trying to satisfy performance metrics over time. Randomized simulations show that this decentralized control approach guarantees efficiency, safety, and a higher throughput than traditional solutions.

Keywords: Cooperative autonomous systems, Hybrid systems, Decentralized control
Abstract: The paper presents a hybrid formalism to analyze a decentralized control policy for a network of agents characterized by a class of non-linear heterogeneous dynamics and biased measurements. Each agent has a continuous-time dynamics and tracks a piecewise constant impulsive reference. The reference jumps depend on the relative distance with respect to some time-varying neighbors. The jumps take place at some updating instants that are decided independently by each agent. Our results provide input-to-state stability guarantees as far as a minimum and maximum dwell-time condition between consecutive updates is satisfied. A numerical example inspired by the formation realization problem for non-holonomic vehicles illustrates the theoretical results.

Keywords: Maritime, Cooperative autonomous systems, Optimal control of communication networks
Abstract: This paper presents the development of a networked control system for maritime applications within the joint project GALIELOnautic to enable autonomous shipping and optimal maneuvering in harbor environments. To achieve this objective, all networked vehicles estimate position, velocity, orientation and time dynamically using a maritime navigation filter. These vehicle states are broadcasted to a central computation unit via long term evolution (LTE) mobile communication in combination with virtual private network (VPN). After a data synchronization step, an optimization algorithm uses the vessel states to calculate model-based optimal trajectories for each networked participant. These trajectories consist of taget positions at certain instants of time and optimal settings for the rudder and propulsion units which are used as feedforward variables for a local trajectory controller on each vessel. To compensate smaller errors due to model inaccuracies during trajectory calculation, a feedback control loop is also established. Beside active network participants, other vehicles can be considered as well using data which is broadcasted by the automatic identification system (AIS). This paper presents the promising results of GALILEOnautic demonstrating an encounter situation of two networked vessels and one disturbing, non-networked participant. To demonstrate how the approach is able to avoid critical situations, three different demonstrators with increasing complexity are shown: networked robots, unmanned surface vehicles, and a ship-handling simulator.

Keywords: Cooperative control, Stability of linear systems, Robotics
Abstract: The second-order practical consensus tracking of multi-agent systems with a fixed topology and a varying leader’s velocity is obtained by using a linear control strategy and the input-to-state stability framework. Then, a new control protocol is proposed where the leader, called a perceptive leader, receives information from its followers. Finally, a study is provided in simulation and on a fleet of mobile robots showing the effectiveness of non perceptive and perceptive strategies in terms of speed of convergence, precision and connectivity maintenance.

Keywords: Decentralized control, Constrained control, Agents networks
Abstract: Models of multi-agent systems can be found all around us. One of the objectives of multi-agent systems is to define local control laws in order to achieve a desired global state of the system. This paper utilises the concept of Distributed Resource Allocation (DRA) approach for successful incorporation of a large number of Plug-in Electric Vehicles (PEVs) with the power grid. DRA approach is implemented using the concept of achieving output consensus. A fixed number of PEVs are considered which are connected to the grid for a certain time interval. The PEV batteries can be charged as well discharged during this time interval. Charging and discharging of PEVs from the grid is further divided into time slots and are coined as strategies. The goal of this paper is to obtain a well-inclined charging strategy for each PEV connected to the grid such that it satisfies the power grid objective in terms of the smoothening factor of grid load profile.

Keywords: Optimal control, Cooperative autonomous systems, Transportation systems
Abstract: We study route-planning for Autonomous Mobility-on-Demand (AMoD) systems that accounts for the impact of road traffic on travel time. Specifically, we develop a congestion-aware routing scheme (CARS) that captures road-utilization-dependent travel times at a mesoscopic level via a piecewise affine approximation of the Bureau of Public Roads (BPR) model. This approximation largely retains the key features of the BPR model, while allowing the design of a real-time, convex quadratic optimization algorithm to determine congestion-aware routes for an AMoD fleet. Through a real-world case study of Manhattan, we compare CARS to existing routing approaches, namely a congestion-unaware and a threshold congestion model. Numerical results show that CARS significantly outperforms the other two approaches, with improvements in terms of travel time and global cost in the order of 20%.

Keywords: Control education, Control courses and labs, Computer aided learning
Abstract: In the literature on control education, a minority of papers concern logic control. Out of this minority, an even smaller one addresses the problem of designing an application composed of both modulating and logic control functions, interacting and coordinating with one another. In the opinion of the authors this is a gap to be filled, as it easily leads to an inefficient use of the available development tools, and sometimes results in cumbersome design and poor maintainability. In this paper we discuss the matter, and propose a compact teaching activity based on the experience gathered along the last years.

Keywords: Control courses and labs, Control over networks, Control over communication
Abstract: An HTTP communication standard called Server-Sent Events is proposed for use in remote laboratories to allow students in distance education and blended learning scenarios to effectively work with event-triggered and self-triggered control schemes. A tool that integrates such technology to expose on the Internet control programs developed in LabView and make them accessible through web applications is also presented. Both the communication standard and the tool are then used in a remote lab example of a servo-motor built on top of these things.

Keywords: Control education, Computer aided learning, Modeling
Abstract: Nowadays in the digital world there is a need to revisit the content and the methodology of teaching different subjects. Considering the explosion of information available at the internet and the available visual technics and software tools it is required to develop new effective methods for delivering knowledge. Besides static teaching materials interactivity would enhance the effectiveness and also enjoyment of the learning process. Teachers and also students can contribute to developing the learning material. The Open Content Development (OCD) with active teacher/student participation is a project at the Department of Technical Education at the Budapest University of Technology and Economics (BME) supported by the Hungarian Academy of Sciences (MTA). The Sysbook multilevel e-book about systems and control (http://sysbook.sztaki.hu/) provides a platform which is connected to the OCD model. Some case studies demonstrate how these disciplines are applied (e.g. driving a car, energy production, oil refinery, human body, education system). Applying the OCD concept the students may elaborate case studies of other systems and their control as well, and after an expert evaluation their results can be published in the student area of Sysbook.

Keywords: Control education, Computer aided learning, Control courses and labs
Abstract: This paper introduces a survey for the global control community on the most important topics that should be covered when engineering students take just a single control related course, a situation typical for many undergraduate engineering programs. There has been a rapid increase in the availability and power of both computing and internet resources which has an inevitable affect on both what content and how university education is delivered. This paper provides some context and discussion of a preliminary survey with a limited exposure; the intention is that feedback on this paper and the initial survey results will be used in planning for a more comprehensive survey of the entire IFAC community.

Keywords: Control education, Mechatronics, Control courses and labs
Abstract: In this paper we propose a laboratory experience which is designed to effectively teach the automatic control of mechatronic systems during the course of Laboratory of Mechatronics, at the University of Brescia. We propose the use of a laboratory-scale overhead crane to implement a motion control task. The proposed machine is entirely built by using standard industrial hardware, and includes both mechanical and electrical/electronic parts. This allows the students to experience first-hand the importance of many concepts learned in previous units such as PID control, cascade control, and motion planning. A peculiarity of this course is that we propose a non-standard technique for the design of the reference signal, thus exposing the students to a methodology that is normally prerogative of experienced professionals and researchers in automatic control.

Keywords: Control education, Computer aided learning, Control courses and labs
Abstract: This paper investigates methods for quantitatively assessing the importance and relative importance of concepts taught in a university program. This assessment has many uses, e.g., to aid program design and inventory, and for communicating what concepts a course may rely on at a given point in the program.

We propose to perform this quantitative assessment in two steps: first, representing the university program as an opportune graph with courses and concepts as nodes and connections between courses and concepts as edges; second, by quantitatively defining each concept's importance as its centrality as a node within the network.

We thus perform two investigations, both leveraging a practical case -- data collected from two engineering programs at two Swedish university: a) how to represent university programs in terms of graphs (here called Courses-Concepts-Graphs), and b) how to reinterpret the most classical graph-theoretical node centrality indexes in the pedagogical term of concept centrality index.

Keywords: Automotive, Sliding mode control
Abstract: The increasing complexity of the engine control systems and its integration with vehicle dynamics control has led to torque-based management of internal combustion engines. The engine speed depends on the torque demand and load torque acting on the engine. In this research work, a dual loop control strategy is proposed to track the desired speed profile for engine torque management. The air control loop has throttle angle as the control variable, whereas speed control loop has injected fuel mass flow rate as the control input. Smooth Super-Twisting Algorithm based control methodology is developed for tracking the desired speed profile. The novel First Principle based Engine Model is employed to formulate the control scheme and compute net piston force which gives direct measure of the engine torque. Effectiveness of the proposed control strategy is demonstrated via numerical simulations.

Keywords: Sliding mode control, Fault diagnosis, Robotics
Abstract: This paper deals with the formulation of a fault diagnosis strategy for industrial robotic manipulators. The core of the proposed scheme is the inverse dynamics-based feedback linearized robotic MIMO system, that is a set of linearized decoupled SISO systems affected by uncertain terms. Relying on this set of systems, in the paper the problem of detecting and isolating both sensor and actuator faults is considered. The proposed fault diagnosis strategy consists of a vision based logic, to detect possible malfunctions of the sensors of the robot, and a set of Unknown Input Observers (UIOs) of second order sliding mode type, to perform the Fault Detection and Isolation (FDI) on the actuators. The sliding mode approach provides good performance in terms of stability and robustness, while guaranteeing a satisfactory estimate of the faults. To give the possibility to the fault diagnosis system to distinguish between sensor and actuator faults, a vision sensor is used in the scheme. This sensor also allows to design a fault tolerant control strategy in case of sensor faults. The verification and the validation of the present proposal have been carried out, both in simulation and experimentally, on an industrial robotic manipulator.

Keywords: Sliding mode control, Observers for linear systems, Fault estimation
Abstract: In this paper, a sliding mode based observer design for strongly observable systems in the presence of non-differentiable unknown inputs is presented. The system state is reconstructed in finite time by means of a linear observer in combination with a robust exact differentiator. By appropriate filtering of the measured output, differentiation of signal components involving the unknown input is avoided. For strongly detectable systems, the proposed technique is combined with invariant subspace methods to provide asymptotic reconstruction of the state. A numerical simulation example demonstrates the performance of the proposed approach.

Keywords: Sliding mode control, Robotics, Adaptive systems
Abstract: In this paper, an adaptive sliding mode dynamic positioning control approach is proposed for a semi-submersible offshore platform. The actuator dynamics are slow and thus a first order sliding mode control approach is used to maximise tracking accuracy in the presence of typically unmodelled actuator dynamics. The sliding mode control is designed with an adaptive feedback gain to counter the effects of model uncertainty and external disturbances such as the waves. The control implementation uses a sliding mode differentiator for online estimation of velocity and acceleration. The stability of the system is analyzed using Lyapunov methods. The control algorithm is validated using illustrative examples.

Keywords: Sliding mode control, Robust control, Uncertain systems
Abstract: It is well known that higher order sliding mode controllers (HOSMCs) are robust against perturbations but consume large amounts of energy which is mainly due to the chaterring of the control input. This paper proposes a controller allowing to keep the robustness while reducing the energy consumption. This is made by introducing a time varying parameter α on the exponent term of the controller. The finite time convergence of the closed-loop system to a vicinity of the origin is established. Finally, simulations that validate the effectiveness of the proposed controller are presented. An additional result concerning the use of a different law for varying alpha is introduced.

Keywords: Sliding mode control, Adaptive control, Autonomous systems
Abstract: In this paper, we consider the adaptive integral super-twisting sliding mode control problem for stochastic systems with bounded uncertainties and state-multiplicative stochastic (Brownian) noise. We first introduce an integral-type sliding surface. Then, we develop the adaptive super-twisting controller which guarantees the reachability to the specified sliding surface in finite time. We also characterize a sufficient condition for stochastic stability of sliding motion in terms of a linear matrix inequality. Hence, the proposed controller first guarantees the finite-time convergence to the sliding surface and then achieves the stochastic stability of the closed-loop system in the predefined sliding surface under bounded uncertainties. Two simulation results are presented to illustrate the theoretical results and the effectiveness of the proposed control method.

Keywords: Chemical process control, Observers for nonlinear systems
Abstract: This paper proposes a method to diagnose unexpected changes in the dynamic behavior of Chemical Reaction Network models. It is considered that the disturbances can induce changes in the reaction rate coefficients of chemical reactions. Conditions for the estimation of such disturbances are formulated. Using the algebraic properties of kinetic models, on-line observers are designed to monitor the disturbance-generated modifications in the reaction rate coefficients. An extended disturbance observer is also introduced for such cases when not all the states of the Chemical Reaction Network are measurable. The applicability of the developed method is shown through simulation studies.

Keywords: Modeling, Process control, Identification for control
Abstract: A non-linear first principle model of an industrial methane rich gas (MRG) network for a large petrochemical plant is developed in this paper. The model is validated against industrial plant data. The non-linearity of gas compressibility, which is sensitive to changes in pressure, composition and temperature, is a challenge for the control of such a network. This model addresses these non-linearities, and is validated with industrial plant data. Each subsystem in the network is modelled separately, and includes models for the pipe segments, gas compressibility, pipe friction factor and gas viscosity. A first principle non-linear model of the system will assist the design of supervisory and optimisation controllers which remain relevant over a large operating region.

Keywords: Process control, Chemical process control, Discrete event systems
Abstract: In this paper, the application of a Symmetric-Send-on-Delta event-based proportional-integral controller to a microalgal raceway photobioreactor is presented. It is shown that the event-based mechanism can be simply added to a classical time-driven PI controller, what allows simplifying the overall design of the event-based approach. The influence of the Delta threshold value is evaluated, and the results of the event-based architecture are presented in simulation and compared with a standard time-based PI controller. It is demonstrated that the event-based controller achieves the required performance by reducing the use of the actuator and saving costs related to gas consumption.

Keywords: Stability of nonlinear systems, Delay systems, Process control
Abstract: A new stabilizing feedback design method is proposed in this paper for time delayed nonnegative polynomial systems with a linear input structure. Using a polynomial state feedback, the open loop system is transformed into a complex balanced kinetic system that is known to be stable with a known Lyapunov-Krasovskii functional. The computation problem is solved through semidefinite programming exploiting the fact that the reaction graph structure and weighting of a kinetic polynomial system are non-unique.

Keywords: Chemical process control, Predictive control for nonlinear systems, Stochastic systems
Abstract: Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant stochastic uncertainties that can lead to closed-loop performance problems and infeasibility issues. In this paper we propose a novel stochastic NMPC (SNMPC) algorithm to optimize a probabilistic objective while adhering chance constraints for feasibility in which only noisy measurements are observed at each sampling time. The system predictions are assumed to be both affected by parametric and additive stochastic uncertainties. In particular, we use polynomial chaos expansions (PCE) to expand the random variables of the uncertainties. These are updated using a PCE nonlinear state estimator and exploited in the SNMPC formulation. The SNMPC scheme was verified on a complex polymerization semi-batch reactor case study.

Keywords: Process control, Model/Controller reduction, Output feedback

Keywords: Cooperative autonomous systems, Intelligent systems, Control over communication
Abstract: In this paper, we study the effect of packet losses in Vehicle-to-Vehicle (V2V) communication on string stability of a platoon of vehicles employing a Cooperative Adaptive Cruise Control (CACC) system. Such vehicles employ a Constant Time Headway Policy (CTHP) and utilize communicated information of the acceleration of preceding vehicle in the string. This communication is subject to packet losses in a realistic wireless system and we model the packet loss phenomenon as a binomial random process characterized by a packet reception probability. We assume that the platoon is homogeneous and derive the inverse relationship between the minimum string stable time headway and the packet reception probability. Numerical simulations corroborate the results presented in this paper

Keywords: Transportation systems, Autonomous systems, Game theoretical methods
Abstract: This paper proposes a controller design framework for autonomous truck platoons to ensure safe interaction with a human-driven car. The interaction is modelled as a hierarchical dynamic game, played between the human driver and the nearest truck in the platoon. The hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-horizon behaviour. The hierarchical approach enables feasible computations where human uncertainties are represented by the quantal response model, and the truck is supposed to maximise its payoff. The closed-loop control is validated via case studies using a driving simulator, where we compare our approach with a short-horizon alternative using only the tactical horizon. The results indicate that our controller is more situation-aware resulting in natural and safe interactions.

Keywords: Stability of nonlinear systems, Transportation systems, Traffic control
Abstract: This paper studies platoon systems consisting of a mixture of fully automated and human-driven vehicles, modeled via the vehicle-following model. Automated vehicles can transition between manual and automated modes. We present a sufficient condition for mathcal{L}_{infty} input-state stability of these systems. Our result is practically discussed and the effectiveness of the condition is illustrated via numerical examples.

Keywords: Transportation systems, Optimization, Autonomous systems
Abstract: Considering the target of an operating effectively transportation system, we propose here a novel methodology for integrated lane-changing and ramp metering control that exploits the presence of connected and partly automated vehicles. In particular, we assume that a percentage of vehicles can receive and implement specific control tasks (e.g., lane-changing commands). A novel approach is designed to robustly maximise the throughput at motorway bottlenecks employing a Linear Quadratic Integral (LQI) regulator in combination with an anti-windup scheme, based on a simple Linear Time Invariant (LTI) model. The method is evaluated via simulation experiments, performed on a first-order, multi-lane, macroscopic traffic flow model, also featuring the capacity drop phenomenon, which allows to demonstrate the effectiveness of the developed methodology and to highlight the improvement in terms of traffic efficiency.

Keywords: Transportation systems, Traffic control
Abstract: In this paper we present an average study between the continuous-time signalized T-periodic Link Transmission Model (LTM), and its averaged version. Those are macroscopic models capturing the time-evolution of the vehicles densities in urban traffic networks controlled by periodic traffic light of period T. In this paper, we formalize the mathematic sense in which the solutions of the periodic signalized LTM model are approximated by the solutions of its averaged version. In particular, we shown that the error norm between the solutions of the signalized and the averaged models is bounded, in both a finite and infinite time-intervals, by constant proportional to the the ratio, T/L, between the traffic light time-cycle T, and the considered road segment (link) length, L. This result confirm the intuition that the precision of the averaged models improves with the increase of traffic light frequencies and link road lengths.

Keywords: Autonomous systems, Emerging control applications, Transportation systems
Abstract: Lateral control of an autonomous vehicle, especially in emergency situations, is important from the safety viewpoint. In these situations, the trajectory to be followed by a vehicle must either be planned in real-time (e.g., for a possible evasion maneuver if the obstacle to be avoided is detected) or be communicated from its predecessor. Typically, the trajectory information is available to the following vehicle in the form of GPS time samples. From the viewpoint of lateral control, the lateral velocity information is not readily available and the feedback structure must reflect this reality. In this paper, we develop a methodology to synthesize lateral control algorithm for autonomous vehicles in two steps: (1) From the limited preview information of the trajectory to be tracked via samples of GPS waypoints, we estimate the radius of curvature of the trajectory using least square estimation and (2) develop a fixed-structure feedback control scheme for following the predecessor by synthesizing the set of stabilizing gains corresponding to lateral position error, heading error and heading rate error. Numerical simulation results corroborate the effectiveness of the proposed schemes.

Keywords: Discrete event systems, Supervisory control, Robotics
Abstract: This paper proposes a hybrid structure for autonomous navigation of multiple robots with tasks assigned by a centralized scheduler. Considering the robots as Discrete Event Systems (DES), the Supervisory Control Theory (SCT) is used to model and control individually the behavior of each robot of the system. Particularly, gathering deliberative and reactive motion planning algorithms through a structured procedure, it allows to obtain a hybrid planner able to perform safe navigation in a mapped workspace with unknown obstacles. Therefore, initially, the open-loop behavior and the reduced supervisors for the robots are provided and, later, a pseudo-code describing the scheduler and its interaction with the robots is presented. Furthermore, as a case study, simulations considering mobile robots are carried out to corroborate the proposed framework.

Keywords: Supervisory control, Discrete event systems, Automata
Abstract: High-tech systems often contain a few components with many dependencies across the system, acting as system-level integrating components. In a hierarchical system decomposition, such so-called bus components tend to be placed in the top node. In supervisory control synthesis, this results in a significant increase of the state space. In this paper, a method is proposed to transform a set of plant models and requirement models into a tree-structured multilevel discrete-event system with a bus structure. After recording the dependencies within the system, the bus and the non-bus plant models are identified and separated. Subsequently, each set of plant models is clustered separately, resulting in a multilevel discrete-event system. Finally, these two systems are merged into a single multilevel discrete-event system where bus plant models are distributed over the multilevel discrete-event system. Experimental results of several models available in the literature are reported to assess the applicability of the proposed method.

Keywords: Stochastic systems, Model/Controller reduction, Discrete event systems
Abstract: This paper proposes a scheme for the construction of discrete-time finite abstractions (a.k.a. finite Markov decision processes) from continuous-time stochastic control systems and quantifying their probabilistic distances. The scheme is based on the notion of stochastic simulation functions enabling us to relate continuous-time stochastic systems with their discrete-time counterparts. Accordingly, one can employ discrete-time abstract systems as substitutions of the continuous-time ones in the controller design process with guaranteed error bounds on their output trajectories. In the first part of the paper, we quantify the distance in probability between original continuous-time stochastic control systems and their discrete-time (finite or infinite) abstractions. In the second part of the paper, we construct finite abstractions together with their corresponding stochastic simulation functions for a particular class of stochastic affine systems having some stability property. We demonstrate the effectiveness of the proposed results by applying our approaches to the temperature regulation in a building of two adjacent rooms and constructing a discrete-time abstraction from its original continuous-time dynamic. We employ the constructed discrete-time abstraction as a substitute to synthesize policy regulating the temperature of rooms for a bounded time horizon.

Keywords: Discrete event systems, Fault diagnosis, Fault detection and identification
Abstract: We study failure prediction (or prognosis) in discrete event systems using decentralized architectures. We first identify three basic prognoses as the simplest language-based decentralized prognosis architectures. On the other side, inference-based prognosis generalizes several decentralized prognoses, among which the three basic prognoses. Then we propose arborescent prognosis, a framework that generates and uses a tree-like decentralized prognosis architecture. Each node n of the tree is a conjunction or disjunction of the decisions of the two children of n. We show that if inference-based prognosis is applicable to the prognosis objective, then each leaf of the obtained tree is a basic prognoser. This means that by combining basic prognoses, we can realize every prognosis objective that is realizable by inference-based prognosis.

Keywords: Discrete event systems, Petri nets, Supervisory control
Abstract: This paper is about the incremental computation of control sequences for complex discrete event systems (DES). Transition-timed Petri nets (T-TPNs) that behave under earliest firing policy are used to model hybrid FMS that has some operations with total precedence constraints and other operations with full routing flexibility. The objective is to find a control sequence from an initial state to a reference one in minimal time. For that, a cost function is proposed and used with a new variant of beam search method that selectively explores the PN state space.

Keywords: Discrete event systems
Abstract: Internal signal representation is critical for accurate perception, memory, and rapid recall in natural intelligence. In this paper we consider a model for cognitive computation that has as its base an algorithm for converting spatial signals into temporal representations. The model is motivated by vision functions but is supported by applications to concept abstraction and analogical thinking, as well as experimental comparison with the pixel intensity representation of visual images. In fact our method has demonstrated ability to defend against a well-known adversarial attack on the MNIST digits recognition. Ordinal optimization is important in achieving quickness of the algorithm.

Keywords: Optimal control, Optimization algorithms, Nonlinear system theory
Abstract: This work considers the problem of obtaining analytical expressions of optimal control laws for scalar non-linear systems. For a nonlinear system, usually the optimal control laws are computed using iterative/sequential methods. To circumvent this issue, Krotov sufficient conditions, are employed to compute analytical optimal control laws. These conditions work upon the idea of translating the optimal control problem to an optimization problem by using the so-called extension principle and an ad-hoc selection of a function called Krotov function. Subsequently, an iterative solution of this optimization problem is computed. The angle of our attack is modify these equivalent optimization problems via suitable selection of Krotov function so as to obtain a non-iterative solution. The sufficient conditions for the existence of analytical expressions for optimal control laws for a scalar nonlinear system (affine in the control input) with a quadratic cost are derived. The proposed method is illustrated by numerical examples.

Keywords: Optimal control, Biomedical systems, Modeling
Abstract: The hepatitis B virus (HBV) infection represents a global health problem; it can be transmitted through contact with infected blood or other body fluids and it is dangerous being highly contagious. Hepatitis B is preventable with effective vaccine, as it is strongly recommended by the World Health Organization; its protection is possibly life-long, at least 20 years. Mathematical modelling is a useful tool to study the evolution of an epidemic spread and possibly to suggest suitable control strategies, aiming at the best resource allocation while satisfying the obvious limitations, both from economics and practical points of view. In this paper, a recently proposed model describing the HBV infection is adopted and optimal control strategies are discussed; it is enhanced the dependence of the result on the preliminary choice of the cost index, suggesting a rationale in the determination of the weights of the elements to be minimized.

Keywords: Optimal control, Linear systems
Abstract: In this paper, a double integrator traveling sales- man visiting a given set of points in phase plane and coming back to the starting point is considered. Also, each point is visited only once during a tour. The problem of finding the time optimal tour through a particular distribution of points in the phase plane is addressed. An expression for optimal time of travel for optimal tour of double integrator for this "particular" class of distribution of tour points in the phase plane is obtained. The distribution of points for which the method is valid is characterized in terms of a family of parabolas in phase plane.

Keywords: Optimal control, Autonomous systems, Modeling
Abstract: This paper presents a control architecture for autonomous driving near a highway exit. In particular, detailed description is provided for the two blocks in the proposed architecture; a novel trajectory planner for lateral and longitudinal motion, and a novel decision manager for choosing the optimal target lane. We show that the trajectory planning can be realized by executing two sequential quadratic programs as two model predictive controllers, where one is planning the optimal trailing of a leading vehicle and the other is planning a lane change maneuver. Having the two optimal trajectories and a scalarized objective that considers safety, comfort, reference tracking and distance to the exit, the decision manager chooses one of the planed trajectories that is send to the vehicle. A case study is provided showing the effectiveness of the proposed control architecture.

Keywords: Optimal control, Randomized algorithms, Nonlinear system theory
Abstract: This paper deals with the design and analysis of open-loop soft-landing control policies for a class of nonlinear actuators. A third-order nonlinear parametric model is firstly presented and the particularities of the systems under study are highlighted. Then, time-optimal and energy-optimal trajectories are analytically derived by means of the Pontryagin principle. Numerical solutions are obtained for a nominal model and the robustness of the obtained open-loop input profiles on perturbed systems is studied via Monte Carlo simulations. The results show that the impact velocities are efficiently reduced with any of the proposed strategies and, consequently, that open-loop control could be a practical and cost-effective approach for improving the performance of these actuators.

Keywords: Optimal control, Variational methods, Autonomous systems
Abstract: The autonomous vehicle is one of the greatest challenges in modern vehicle design. This paper proposes a new technique to define the optimal trajectory in a feedback form for an autonomous car, moving on a track. The algorithm defines the trajectory taking into the account the vehicle dynamic instead of kinematic constraints, leading to a more robust path. The technique is also used to control the vehicle in feedback, providing the optimal maneuvers to track the defined path. The method is based on the control law named FLOP, Feedback Local Optimality Principle, recently developed by the authors. The technique, starting from the Pontryagin’s theory, introduces a new optimality principle that minimizes a sequence of individual functionals with the chance of a direct feedback control. The control acts on the steering and torques of a two-wheeled vehicle.

Keywords: Linear systems, Neural networks
Abstract: We investigate the property for an input-output system to map unimodal inputs to unimodal outputs. As a first step, we analyse this property for linear time-invariant (LTI) systems, static nonlinearities, and interconnections of those. In particular, we show how unimodality is closely related to the concepts of positivity, monotonicity, and total positivity.

Keywords: Robotics, Neural networks, Machine learning
Abstract: State-of-the-art robotic manipulators come with functions that detect unforeseen contact situations with the environment. However, for collaborative manipulators that are specifically designed to allow physical Human-Robot Interaction (pHRI), contact situations can be either intended (the operator may intentionally interact with the robot for instance to modify its trajectory) or undesired (a human accidentally runs into the robot). An appropriate reaction strategy for the robot manipulator depends on a correct classification and localization of the contact situation. To this end, this work proposes an approach using supervised learning techniques to distinguish between unintended contact situations (labeled collisions) and foreseen ones (labeled interactions), and to infer whether a contact occured on the upper or lower arm of the robot. A neural network is trained on measurement data gathered from different contact situations between a human subject and an ABB YuMi robot. The proposed method is then evaluated online on the robot using simple reaction strategies on both the person who trained the network and on other human individuals.

Keywords: Computational methods, Modeling, Intelligent systems
Abstract: The quality of agricultural sprays plays an important role in the application of chemical products dedicated to plant protection and food production. In such context, the effects of nozzle type, standard and size, as well as the operating pressure should be well observed. Measured droplet sizes and velocities are also important, and both can be affected by nozzle type, size, and operating pressure. Therefore, control aspects have become necessary to improve quality and to reduce undesirable externalities in the agricultural environment. This paper, presents a soft-sensor based on the k-NN classifier and estimator, which requires an attribute vector with the spraying quality descriptors, and delivers an estimation for the best values of the descriptors to obtain an adequate quality in relation to the spraying in agricultural pest control. The results of this research reveal the usefulness of such soft-sensor architecture based on estimation theory and added value in the application of agricultural pesticides.

Keywords: Emerging control applications, Neural networks
Abstract: In this paper we introduce a pitch wave force predictor for a wave energy converter. The designed predictor is composed of two subsystems. First, we design a virtual sensor in order to provide real-time estimate of the pitch wave force acting on the converter, from measurements provided by a set of on-board sensors. Then, we build an autoregressive predictor on such a virtual measurement to predict the future value of the wave force over a finite length prediction horizon. Simulation results, performed by applying the proposed algorithm on a first-principles based Simulink model of the considered sea wave energy converter, demonstrate the effectiveness of the proposed approach.

Keywords: Fault diagnosis, Neural networks, Switched systems
Abstract: This paper presents a fault diagnosis approach for single open-circuit faults in inverters entirely from measurements of the stator currents. These measurements are used to extract the feature data; the feature data is then used to create clusters in an online, adaptive and unsupervised way. Auto-adaptive and Dynamical Clustering (AUDyC) is the algorithm employed for this step. Based on the derived clusters, appropriate formulations for the data labelling and fault detection and isolation are proposed. The effectiveness of the approach is validated on simulation and experimental data.

Keywords: Machine learning, Neural networks, Filtering
Abstract: The paper investigates the trajectory tracking problem of a flight vehicle performing complex maneuvers. A learnable Extended Kalman Filtering (L-EKF) method is proposed. First, two recurrent neural networks, named Input Modification Network and Gain Modification Network, are designed to identify the model inaccuracy and compensate for the estimation error of the EKF. Then, the L-EKF algorithm is proposed by embedding the two networks into the EKF algorithm. Then, the proposed L-EKF method is applied to a trajectory tracking problem of a gliding vehicle with complex maneuvers. The simulation results show that the proposed method has a higher estimation accuracy and better dynamic performance than the EKF and Adaptive EKF method.

Keywords: Linear parameter-varying systems, H2/H-infinity methods, Optimization algorithms
Abstract: This paper presents an approach to combine H∞ dynamic output feedback controllers for linear parameter-varying (LPV) systems with optimal selection of sensors and actuators. This concurrent design problem is formulated as a Mixed Boolean Semi-Definite Programming (MBSDP) optimization problem. By means of the Big-M reformulation, the boolean decision variables related to the selection of sensors and actuators are decoupled from the SDP problem underlying the H∞ controller synthesis. This reformulation allows us to solve the initial NP-hard problem using a Branch and Bound (BNB) algorithm. The concurrent design of an LPV vibration controller and optimal actuator selection for a smart composite plate serves as a case study to validate the developed approach.

Keywords: Linear parameter-varying systems, Nonlinear system identification, Identification
Abstract: This paper brings B-splines into LPV system identification to express the model dependency on the scheduling parameter(s) and presents a strategy to control the complexity of this dependency. The higher the number of B-splines, the more complex the model. Since the number of B-splines building a spline is proportional to the number of knots partitioning the domain over which the spline is defined, the core of the strategies proposed in this paper is the selection of the optimal number and locations of the knots. A dedicated measure of significance of a knot combined with a unique grouping of the model state-space matrices and l2,1-norm regularization yielded two new methods: the Regularized State-space Model Interpolation of Local Estimates and the Regularized NLS-based LPV system identification approach.

Keywords: Linear parameter-varying systems, Output feedback
Abstract: In this paper, a design approach for a static linear parameter-varying (LPV) output feedback controller is proposed. Based on an approach with linear matrix inequalities (LMIs) for linear time invariant (LTI) systems, a synthesis procedure is obtained. Designing static output feedback controllers in general leads to a bilinear matrix inequality (BMI) problem, which is a nonconvex problem and rather difficult to solve numerically. By introducing additional auxiliary variables, the design problem can be transformed into a convex LMI problem for the LTI case. Compared to nonconvex BMI problems, convex LMI problems can be solved very efficiently. In this work this approach is extended to LPV systems. Furthermore, the approach is applied to a nonlinear control example in order to show the applicability of the presented design procedure.

Keywords: Linear parameter-varying systems
Abstract: The aim of this study is to design a suitable LPV (Linear parameter varying) controller for large wind turbine operating in high winds, with the objective to regulate the generator speed at the nominal value, and to alleviate the aerodynamic loads induced on the tower structure by wind forces. This LPV controller is synthesized by minimizing the H-infinity norm of the closed-loop system and is gain scheduled according to the pitch angle in real time. The performance of the proposed controller in reducing the tower loads is demonstrated on the FAST simulator for the NREL 5MW wind turbine, and is compared to the classical baseline PI (Proportional-integral) to show that an improvement in performance is obtained for the entire operating range of the wind turbine.

Keywords: Robotics, Linear parameter-varying systems, Maritime
Abstract: The paper deals with the issue of developing an attitude control system for a remotely operated vehicle (ROV) for large inclination angles (pitch, roll). The construction of the orientation control system is considered based on the traditional approach using Euler angles (yaw, pitch and roll). A transfer matrix general form of the ROV attitude control system was obtained. It was shown that with the increase of inclination angles the transfer matrix of the system becomes multivariable. Algorithms of disturbances compensation and a methodology for parameters selection for decomposition algorithm were proposed. The obtained results were verified during the in situ tests of the ROV “Iznos”, developed in Bauman Moscow State Technical University (BMSTU). The proposed decomposition algorithms allow to expand the ranges of working angles and to improve the quality of the control system performance without significant re-engineering of its structure.

Keywords: Stability of linear systems, Linear systems, Linear parameter-varying systems
Abstract: Youla parametrization of stabilizing controllers is a fundamental result of control theory. It provides a theoretical and practical tool that renders LFT loops into model matching framework. Based on the geometric techniques introduced in our previous work we have provided a controller blending rule that defines a controller semigroup structure based only on the knowledge of the plant and a single stabilizing controller and we have introduced a novel, geometry based parametrization. In this paper we investigate the possibility to construct stabilizing fractions of the given stabilizing controller relative to the blending rule. We also extend our geometry based framework to the LFT loops. Our main goal is to show that every controller which stabilizes the interior loop also stabilizes the LFT loop. Contrary to the expectations, this problem is far from being trivial.

Keywords: Predictive control for nonlinear systems, Energy systems
Abstract: Modern wind turbine control has a great demand for advanced control strategies that deal with several, divergent objectives, such as load alleviation and power leveling, simultaneously. Model predictive control can handle such multivariable control problems and enables the use of preview wind speed information to improve the control performance. In this work, we address the inclusion of available wind speed predictions in the control formulation and derive specifications for wind speed prediction trajectories. To this end, we propose a linear time variant model predictive control formulation and investigate the influence of the wind speed measurement accuracy and the impact of different prediction update times on the control performance. According to our simulations, a wind speed measurement accuracy <5% seems to be appropriate for model predictive wind turbine control using the persistence method for wind speed prediction. Prediction updates within the prediction horizon can improve the control results but may require a retuning of the controller parametrization.

Keywords: Predictive control for nonlinear systems, Optimization algorithms, Observers for nonlinear systems
Abstract: This paper addresses the problem of performance monitoring for Economic Model Predictive Control (EMPC) in the presence of plant parameter changes. In order to cope with plant-model mismatch, we adopt a recently developed offset-free EMPC algorithm which requires the gradient of the plant input-output steady-state map. A subspace identification method is used in order to estimate this plant gradient from transient measurements. However, when the plant parameters change, this method may fail unless re-identification is performed. Hence, to start a new data collection for the identification an event-triggered mechanism is proposed, based on a suitable performance monitoring strategy. In this case this mechanism investigates a possible, more profitable, steady-state equilibrium and, if convenient, it re-identifies the plant gradient. The proposed monitoring technique is then successfully tested over an illustrative example of a chemical reactor.

Keywords: Predictive control for nonlinear systems, Optimization algorithms, Nonlinear system theory
Abstract: Today, various nonlinear programming problem (NLP) solvers and CC++ code generation frameworks are available as open source for solving nonlinear model predictive control (NMPC). Almost all the solvers are written in CC++ code which are more compatible with the PC-based simulation environment. These codes are not directly compatible for embedded implementation and real-time control. An attempt has been made to address this shortcoming by creating a customized framework on top of the C code generated from Acado toolkit to make it directly compatible with ARM based embedded platforms. The study also analyzes the embedded implementation aspects using C code generated for qpOASES, qpDUNES, and HPMPC solvers from acado. We show the results of hardware-in-loop HIL simulations with detailed analysis and comparison of memory requirement and achievable sampling time for three benchmark dynamical systems on different embedded platforms viz ARM Cortex M3, PYNQ FPGA and Raspberry Pi. The results show that qpOASES outperforms as compared to the other two solvers when the computational time is of prime importance. Similarly, when there are limited on-chip memory resources, qpDUNES can prove beneficial.

Keywords: Predictive control for nonlinear systems, Optimization algorithms, Automotive
Abstract: In this paper we introduce MATMPC, an open source software built in MATLAB for nonlinear model predictive control (NMPC). It is designed to facilitate modelling, controller design and simulation for a wide class of NMPC applications. MATMPC has a number of algorithmic modules, including automatic differentiation, direct multiple shooting, condensing, linear quadratic program (QP) solver and globalization. It also supports a unique Curvature-like Measure of Nonlinearity (CMoN) MPC algorithm. MATMPC has been designed to provide state-of-the-art performance while making the prototyping easy, also with limited programming knowledge. This is achieved by writing each module directly in MATLAB API for C. As a result, MATMPC modules can be compiled into MEX functions with performance comparable to plain C/C++ solvers. MATMPC has been successfully used in operating systems including WINDOWS, LINUX AND OS X. Selected examples are shown to highlight the effectiveness of MATMPC.

Keywords: Predictive control for nonlinear systems, Mechatronics, Energy systems
Abstract: The standard material fatigue estimation process is implemented in an Economic Nonlinear Model Predictive Controller. The process comprises cycle identification, mean-stress effect, Woehler curves and Linear Damage Accumulation. For cycle identification, this novel implementation uses a standard Rainflow-algorithm which is substituted periodically by linear functions based on its output. The implementation is compared to two straightforward methods for fatigue penalization using a wind turbine model and exhibits higher profit over the entire range of tested reference wind velocities.

Keywords: Predictive control for nonlinear systems, Cooperative control, Autonomous systems
Abstract: This paper presents a distributed optimization-based control algorithm for the safe navigation of multi-surface vehicles in a complex environment with fixed and moving obstacles. We first generate off-line within the a priori known environment a collision-free path using RRT* (optimal Rapidly-exploring Random Tree) algorithm. Next, potential field constructions are developed through the use of on-off barrier functions to ensure vehicle connectivity and fixed/moving obstacles avoidance. These ingredients are integrated in a distributed NMPC (Nonlinear Model Predictive Control) framework which activates the constraints only in the view range of the agents. The algorithm shows good results in simulations for both connectivity maintenance and validation of the COLREGS rules.

Keywords: Game theoretical methods, Control over networks, Large-scale systems
Abstract: In this paper, focusing on agents' strategic behavior in their private information utilization, we formulate two types of strategic bidding problems for a simple dynamic principal-agent type linear quadratic (LQ) network where a principal integrates agents' individual controls into the socially optimal one based on information bid by the agents. One is the case that all the agents minimize their own cost, which is undesirable for the principal. The other is an equilibrium in the presence of the incentive design motivated by the standard contract theory to reduce the wasteful social cost. Then we analytically derive optimal designs of strategic bidding for each type and discuss their implications and implementations.

Keywords: Game theoretical methods, Distributed control, Large-scale systems
Abstract: We address the generalized Nash equilibrium seeking problem for a population of noncooperative agents playing potential games with linear coupling constraints over a communication network. We consider a class of generalized potential games where the coupling in the cost functions of the agents is linear, i.e., J_i(x_i; x_{-i}) := f_i(x_i) + l_i(x_{-i})^top x_i, where l_i is linear. By exploiting this special structure, we design a fully distributed algorithm with convergence guarantee under mild assumptions, i.e., (non-strict) monotonicity of the pseudo-subdifferential mapping. The potential of the proposed algorithm is shown via numerical simulations on a networked Nash Cournot game, where we observe faster convergence with respect to standard projected pseudo-gradient algorithms.

Keywords: Game theoretical methods, Distributed cooperative control over networks, Agents and autonomous systems
Abstract: We study settings in which autonomous agents are designed with the goal of optimizing a given system-level objective. In typical approaches to this problem, each agent is endowed with a decision-making rule that specifies the agent's choice as a function of the information pertaining to the system's state. A key component of this information is the choices made by other agents in the system. This paper considers the scenario where the designed decision-making rules are not implementable in the realized system due to discrepancies between the anticipated and realized information available to the agents, i.e., the agents are in a situation where they do not have access to all information necessary to execute their designed decision-making rule. Given such instances, this paper asks whether the agents can preserve system-level efficiency guarantees in these unanticipated scenarios through local and independent redesigns of the agents' decision-making rules. First, we show that distributed submodular maximization problems are particularly amenable to local redesigns of utility functions, i.e., agents can adapt to informational inconsistencies independently without incurring much loss in terms of system-level performance. However, for general non-submodular objective functions, our second result demonstrates that there are no local redesign methodologies that can offer any preservation of system-level efficiency guarantees. Accordingly, centralized coordination of a redesign may be imperative for maintaining efficiency guarantees in such systems.

Keywords: Game theoretical methods, Energy systems, Stochastic control
Abstract: Scalable coordination algorithms for neighbourhoods of households featuring electric vehicles (EVs) and embedded renewable generation are required in order to achieve safe and efficient operation of local distribution networks without excessive centralization of control decisions. This paper presents a non-cooperative algorithm to coordinate multi-period EV charging amidst uncertainty affecting both the charging dynamics and the intermittent renewable energy supply. The strategy space includes not only nominal EV charging plans, but also a causal system of linear responses to the forecast errors of renewable infeeds. In a setting where the price of electricity from the main grid has a fixed component and a component that depends on the aggregate purchase requirements of the neighbourhood, we prove a condition under which convergence to the unique Nash equilibrium of the associated game is guaranteed. The algorithm relies on an update rule known as the Krasnoselskij iteration, and the convergence proof is based on the non-expansivity property of an associated operator. We demonstrate the algorithm for a group of 100 households.

Keywords: Game theoretical methods, Optimal control, Computational methods
Abstract: In this paper we study a class of linear quadratic Stackelberg difference games with constraints. We assume that the leader has two sets of strategies. He announces only one set of strategies and the other set strategies are not announced but are constrained. We formulate the necessary conditions for the existence of Stackelberg equilibrium for this class of games. In particular, we show under a few assumptions the necessary conditions can be recasted as a single large scale complementarity problem. We also provide sufficient conditions underwhich the solution of this complementarity problem is a Stackelbreg equilibrium.

Keywords: Game theoretical methods
Abstract: This paper proposes a decentralized hierarchical price function design for charging coordination of Plug-in Electric Vehicles (PEVs) based on the reverse Stackelberg mechanism. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of PEVs and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, the optimal charging strategies of the PEVs are coupled through the electricity price. The PEVs in a group are considered to cooperate in finding their Nash-Pareto optimal charging strategy, by minimizing a social cost function. We propose a decentralized algorithm by combination of mean-filed control and reverse Stackelberg game to find an optimal linear price function while the followers’ strategies converge to eN-Nash equilibrium point of the game.

Keywords: Nonlinear system theory, Robotics
Abstract: Control barrier functions provide a novel methodology to guarantee safety in the context of optimization-based controllers. While the controller synthesis methods are new, barrier functions have a long and rich history from optimization to establishing the invariance of sets. This talk, therefore, gives an overview of barrier functions tracing back to their origins and leading to the modern controlled variant. This will be coupled with the foundational results for control barrier functions establishing necessary and sufficient conditions for safety, and the resulting optimization based controllers that can be synthesized. Finally, an overview of the applications of control barrier functions will be given with an emphasis on robotic systems.

Keywords: Nonlinear system theory, Robotics
Abstract: Control barrier functions are a versatile framework to construct controllers enforcing safety constraints. If we describe the safety constraints by a set of safe states, then control barrier functions allow for the construction of controlled invariant subsets of the set of safe states. Conversely, for any controlled invariant set we can construct a corresponding control barrier. In this talk we will build on the symbiotic relationship between these concepts, control barriers and controlled invariant sets, to describe computational techniques for the construction of controlled invariant sets.

Keywords: Nonlinear system theory, Robotics
Abstract: Given a safety requirement for a dynamical system, finding an appropriate control barrier function can be challenging if the system is subject to actuation constraints. In this talk, we present several methods for computing control barrier functions in such circumstances. The underlying assumption for these methods is the existence of a nominal evasive maneuver for the system from which a control barrier function is obtained. In some cases, the nominal evasive maneuver results in a closed-form control barrier function. In other cases, the control barrier function can be numerically approximated online or a conservative approximation can be obtained with sum-of-squares techniques. We discuss the trade-offs of these approaches and demonstrate our results on several examples including fixed-wing aircraft and autonomous cars.

Keywords: Nonlinear system theory, Robotics
Abstract: Robots are gradually leaving academic laboratories to move towards less structured and more dynamic environments, such as agricultural lands and construction sites. When their deployment on the field involves long-term operations, two key factors have to be considered: robustness against unexpected and unmodeled phenomena, and energy constraints. In this talk, we explore how control barrier functions can be used to address both aspects, and we present a formulation that allows for a robust persistent execution of robotic tasks under limited energy availability constraints.

Keywords: Nonlinear system theory, Robotics
Abstract: Enforcing safety-critical constraints in the presence of model uncertainty is challenging and is needed for enabling dynamic robots to operate in the real world. This talk will introduce exponential control barrier functions as a means to enforce arbitrary high relative-degree safety-critical constraints. This formulation will then be used to enable enforcing safety constraints in the presence of model uncertainty. Applications of the safety-critical controllers on legged and aerial robots will be presented.

Keywords: Nonlinear system theory, Robotics
Abstract: By now, we have a fairly good understanding of how to design coordinated control strategies for making teams of mobile robots achieve geometric objectives in a distributed manner, such as assembling shapes or covering areas. But, the mapping from high-level tasks to these objectives is not particularly well understood. In this talk, we investigate this topic in the context of long-range autonomy, i.e., we consider teams of robots, deployed in an environment over a sustained period of time, that can be recruited to perform a number of different tasks in a distributed, safe, and provably correct manner. This development will involve the composition of multiple barrier certificates for encoding the tasks and safety constraints through Boolean expressions.

Keywords: Nonlinear system theory, Robotics
Abstract: This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems.

Keywords: Adaptive control, Nonlinear system theory, Lyapunov methods
Abstract: The paper addresses the problem of performance improvement of adaptive backstepping control for a class of nonlinear system. Proposed solution to the problem involves an adaptation algorithm with regressor dynamically extended by a linear filter. On the one hand the dynamic extension accelerates the tuning of adjustable control due to the “memory” effect of the filter, but on the other hand it permits to obtain time derivatives of adjustable parameters used in virtual and actual controls up to required order. The actual control is free from overparameterization, does not contain tuning functions and completely compensates nonlinear dynamics and uncertainties of the plant. The efficiency of proposed solution is demonstrated via simulation.

Keywords: Adaptive control, Robotics, Lyapunov methods
Abstract: The inverse dynamics of fully actuated mechanical systems with unknown physical parameters can be represented by linear combination of unknown constants and known time varying functions. This property, which allows the design of adaptive control for fully actuated mechanical systems, does not hold in general for underactuated mechanical systems, such as the acrobot. A control strategy which overcomes this difficulty is presented in recent results, for the case of collocated adaptive control of underactuated mechanical systems. In the present work a new control strategy for generalizing the aforementioned results is introduced and discussed in two examples: non-collocated adaptive control and adaptive virtual constraint of the acrobot.

Keywords: Adaptive control, Robust adaptive control, Stability of nonlinear systems
Abstract: This paper studies the problem of global adaptive control for a class of upper-triangular systems with nonlinear parameterization. We present sufficient conditions under which global adaptive stabilization is achievable by universal-type, gain-dependent nested saturation controllers. In the case of feedforward systems with unknown parameters, our control strategy leads to a new non-identifier based adaptive regulator that eliminates the use of switching adaptive gains, which is not only complicated and less intuitive but also results in discontinuous feedback control laws. Examples with simulation results are provided to validate the effectiveness of the proposed adaptive control method.

Keywords: Adaptive control, Signal processing, Mechatronics
Abstract: An active vibration–suppressed workholding platform (AVSWP) for milling machines is proposed. As to metal cutting, vibration is one of the major factors which affect the surface roughness of finished workpieces. Once the excessive vibration is present during milling process, the surface roughness of the workpiece would be accordingly downgraded. Therefore, AVSWP is proposed to actively suppress the vibration induced by the cutting force. To achieve this goal, an active noise feedback controller based on FxLMS (Filtered-x Least Mean Square) algorithm is applied to achieve satisfactory performance in terms of vibration suppression. According to the experimental results, the vibration of workpiece during milling process can be effectively suppressed by 55% compared with the open-loop case. Besides, from the realistically finished workpieces, it can be observed that the quality of machined surface is upgraded, owing to the vibration of workpiece being successfully suppressed by AVSWP.

Keywords: Maritime, Adaptive control, Autonomous systems
Abstract: This paper deals with adaptive regulation of wave induced vertical motion on a Surface Effect Ship (SES) at zero speed. To this end, a SES is equipped with variable vent valves, through which cushion pressure can be controlled by adjusting the air out-flow from the cushion. Model scale test and sea trials have shown effectiveness of such control system. In this paper, we present an adaptive control system which automatically adjust the tuning parameters for the existing SES Motion Control System. A study of the performance of the control system is demonstrated through numerical simulation using a high fidelity model of SES subject to waves.

Keywords: UAV's, Robust adaptive control, Robotics
Abstract: This paper introduces a quaternion based attitude controller, by augmenting a novel baseline controller with model reference adaptive control techniques (MRAC). In contrast to existing designs, the baseline controller makes use of the quaternion logarithm and the standard bi-invariant distance on SO(3). The adaptation restores the behavior of a time dependent reference model in case of model discrepancies. Moreover, we make use of the fact that the parameters, characterizing the gyroscopic coupling term of a rigid tumbling body, are naturally bounded to some predefined manifold and incorporate this information in our adaptive design. Altogether, the proposed design enables aggressive, high bandwidth acrobatic flight maneuvers over the whole attitude range without singularities, even in the presence of parametric uncertainties. Experimental results on our custom quadrotor platform validate the performance and robustness of the proposed attitude controller.

Keywords: Large-scale systems, Decentralized control, Linear systems
Abstract: We study the disturbance attenuation problem in a heterogeneous mass chain where both the number of masses and the mass distribution may change. The paper studies the scalar transfer functions from the disturbance at a boundary point to a given intermass displacement. It is shown that these transfer functions can be represented in the form of composition sequences generated by Moebius transformations. The framework aims at devising a method of designing the interconnection impedances in the mass chain in a scale-free manner. That is, the resulting design guarantees certain performance criteria for mass chains of any length and any mass distribution. A graphical method is provided for such an interconnection design problem.

Keywords: Large-scale systems, Distributed control, Computational methods
Abstract: It is proved that network realizability of controllers can be enforced without conservatism using convex constraints on the closed loop transfer function. Once a network realizable closed loop transfer matrix has been found, a corresponding controller can be implemented using a network structured version of Internal Model Control.

Keywords: Network analysis and control, Fault tolerant systems, Distributed control
Abstract: We expound recent results introduced in [1] and consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. By investigating the Differentially Private MSR (DP-MSR) algorithm in [1], we present preliminary results on the accuracy of the algorithm in terms of a priori knowledge of some parameters of the faulty nodes. The results are illustrated via simulations.

Keywords: Network analysis and control, Large-scale systems, Linear systems
Abstract: This paper deals with controllability of structured linear time-invariant systems. Controllability of large complex systems are often difficult to attain as a substantial part of nodes in the network require to be actuated and the amount of energy required is large. Moreover, often it is unnecessary to control the entire dynamics of the network: instead, only certain portions of the network need to be controlled. This concept is known as target control. In this paper, we address target control for large complex systems using their network topology. We prove that if there exists one numerical system in the family of the network that is target controllable, then almost all systems in the family are target controllable. This result thus concludes that target controllability is a generic property. Then we propose a bipartite-matching based condition, on a system specific bipartite graph, to determine target controllability of a subset of nodes in a single input network based on the generic rank of the controllable subspace. Finally, we provide experimental analysis to complement our results using real-world data sets.

Keywords: Distributed control, Optimization
Abstract: Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitalized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands.

Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions, hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret.

As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.

Keywords: Distributed cooperative control over networks, Energy systems, Optimization
Abstract: This paper deals with the problem of scheduling home appliances energy consumption in a distributed and cooperative framework. We consider a smart district composed by smart buildings network, where each building is controlled by a local controller agent and can include one or more smart homes. The objective of the district is to find a global optimal scheduling of home appliances through the cooperation of the building controller agents aiming at minimizing the total cost, avoiding power peaks, and taking into account the user preferences in terms of appliances’ priority of usage. In this context, the day-ahead energy agreement should not be violated to avoid increases of real time energy costs. Furthermore, preliminary formal results on the convergence properties of the distributed approach to validate the performance of the proposed method have been provided in a case study.

Keywords: Agents networks, Stochastic systems, Communication networks
Abstract: We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors. Neighbors are defined by a network graph with heterogeneous and stochastic interconnections. These interactions are determined by the “sociability” of each agent, which corresponds to the probability that the agent observes its neighbors. We design an algorithm for each agent to maximize its own expected cumulative reward and prove performance bounds that depend on the sociability of the agents and the network structure. We use the bounds to predict the rank ordering of agents according to their performance and verify the accuracy analytically and computationally.

Keywords: Distributed control, Game theoretical methods, Optimization
Abstract: In this paper, we propose an asynchronous distributed algorithm for the computation of generalized Nash equilibria in noncooperative games, where the players interact via an undirected communication graph. Specifically, we extend the paper "Asynchronous distributed algorithm for seeking generalized Nash equilibria" by Yi and Pavel: we redesign the asynchronous update rule using auxiliary variables over the nodes rather than over the edges. This key modification renders the algorithm scalable for highly interconnected games. The derived asynchronous algorithm is robust against delays in the communication and it eliminates the idle times between computations, hence modeling a more realistic interaction between players with different update frequencies. We address the problem from an operator-theoretic perspective and design the algorithm via a preconditioned forward-backward splitting. Finally, we numerically simulate the algorithm for the Cournot competition in networked markets.

Keywords: Agents networks, Network analysis and control, Lyapunov methods
Abstract: A multi-agent continuous-time nonlinear model of social behaviour allowing for both competition and cooperation is presented and analysed. The state of each agent is represented by its payoff, which the agent aims at maximising. The role of control variables is played by the model parameters, which account for the agents' decisions to either cooperate with or boycott the other agents and can vary in time within assigned intervals. Alliances and enmities can be established at any time, according to either a greedy or a longsighted criterion. The general nonlinear case is first considered. It is proved that, under realistic assumptions, the system evolution is bounded positive (no extinction) and there is a unique globally-stable equilibrium point. As is somehow expected, the optimal decision for all agents corresponds to full cooperation (decision parameters kept at their positive maximum value) in the case of both shortsighted and farsighted criteria. This is not true if some parameters have negative upper bounds (meaning that some agents systematically boycott some others). Then, in the linear case, it is shown that the system is stable for arbitrarily-varying decision parameters, provided that a Metzler matrix associated with full cooperation is Hurwitz. A characterisation of the long-term behaviour of the linear system is also provided. In particular, it is proved that, under stability conditions, a Nash equilibrium exists if a steady strategy is adopted.

Keywords: Game theoretical methods, Electrical power systems, Stability of linear systems
Abstract: This paper deals with the analysis and design of online pricing mechanisms in micro-grids. Two cases are studied in which the market layer is modeled as an open-loop and closed-loop dynamical system respectively. In the case of open-loop market dynamics, the price is generated as equilibrium price of a Stackelberg game with an incentive strategy. In such Stackelberg game, the leader is the energy supplier, the follower is the consumer, and the leader plays an incentive strategy. In the case of closed-loop market dynamics, the price is obtained as a function of the power supplied and the demand. A stability analysis is provided for both cases, which sheds light on the transient and steady-state behavior of the system in terms of the grid’s time constant, inertial, damping and synchronizing coefficients. Conditions on the parameters that guarantee asymptotic stability are obtained for both open-loop and closed-loop configurations. The findings provide an insight on the impact of the time constant and damping coefficient on the system response. The study also elucidates the ways in which the supplier’s decisions influence the output values, thus contributing to clarify the interconnection between the market and physical layers in a micro-grid.

Keywords: Agents networks, Machine learning, Statistical learning
Abstract: Motivated by the increasing interest of the control community towards social sciences and the study of opinion formation and belief systems, in this paper we address the problem of exploiting voting data for inferring the underlying affinity of individuals to competing ideology groups. In particular, we mine key voting records of the Italian Senate during the XVII legislature, in order to extract the hidden information about the closeness of senators to political parties, based on a parsimonious feature extraction method that selects the most relevant bills. Modeling the voting data as outcomes of a mixture of random variables and using sparse learning techniques, we cast the problem in a probabilistic framework and derive an information theoretic measure, which we refer to as Political Data-aNalytic Affinity (Political DNA). The advantages of this new affinity measure are discussed in the paper. The results of the numerical analysis on voting data unveil underlying relationships among political exponents of the Italian Senate.

Keywords: Game theoretical methods, Agents networks
Abstract: In the existing models for finite non-cooperative games on networks, it is usually assumed that in each single round of play, regardless of the evolutionary update rule driving the dynamics, each player selects the same strategy against all of its opponents. When a selfish player can distinguish the identities of its opponents, this assumption becomes highly restrictive. In this paper, we introduce the mechanism of strategic differentiation through which a subset of players in the network, called differentiators, are able to employ different pure strategies against different opponents in their local game interactions. Within this new framework, we study the existence of pure Nash equilibria and finite-time convergence of differentiated myopic best response dynamics by extending the theory of potential games to non-cooperative games with strategic differentiation. Finally, we illustrate the effect of strategic differentiation on equilibrium strategy profiles by simulating a non-linear spatial public goods game and the simulation results show that depending on the position of differentiators in the network, the level of cooperation of the whole population at an equilibrium can be promoted or hindered. Our findings indicate that strategic differentiation may provide new ideas for solving the challenging free-rider problem on complex networks.

Keywords: Autonomous robots, Aerospace, Control over networks
Abstract: Unmanned Aerial Vehicles (UAVs) have been successfully employed in cooperative tasks over recent years, particularly in the applications for smart cities. In such scenario, a networked control system (NCS) framework is usually adopted since measurement and actuation data are mainly transmitted through communication networks. This paper proposes an integrated control architecture for a Cloud-based fixed-wing UAV system, where the control logic resides in the Cloud and sensing and actuating signals are transmitted over a realistic wireless network. The proposed control strategy leverages model predictive control (MPC) and a specialized Kalman filter in combination with two ad-hoc buffers, which enables simultaneous compensation for measurement and control input packet dropouts. Simulations of a nonlinear aircraft model show the effectiveness and advantages of proposed integrated scheme over an existing linear quadratic (LQ)-based control strategy.

Keywords: Autonomous robots, Cooperative control
Abstract: In this paper, we consider a multi-pursuer single-evader pursuit-evasion game where the evader is faster than the pursuers. We show how a faster evader can easily change the angle distribution of the pursuers and give an evader strategy based on this method. We prove that an evader implementing our algorithm is guaranteed to escape against three pursuers following the extension of the parallel guidance law given by Awheda and Schwartz (Awheda, 2016) if the pursuers’ distance from the evader at the start of the pursuit is greater than a lower bound. We perform simulations to confirm these results, both with Dubins-like, and omnidirectional robot models.

Keywords: Autonomous robots, Intelligent systems, Optimization
Abstract: This work is concerned with individuality of intelligent robots. Among variety of individualities the focus is given to the action control individuality for semi-autonomous intelligent robots, and its development and utility are discussed. For the case where complex actions are gradually realized according to the Intelligent Composite Action Control stepwisely from fundamental motions, the action control individuality is introduced and shown to be naturally developed as a result of easiness-based redundancy resolution. And then the utility of action control individualities is discussed as for performance improvement in cooperative tasks.

Keywords: Autonomous robots, Predictive control for nonlinear systems
Abstract: We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.

Keywords: Autonomous robots, Robotics, Hybrid systems
Abstract: To navigate over discrete terrain with large displacements in the stepping locations, bipedal robots have to be able to perform agile and dynamic maneuvers such as jumping or running while also satisfying strict constraints on foot placement and ground contact forces. In this paper, we analyze the problem of bipedal running over stochastically varying discrete terrain with large changes in step lengths. Specifically, our method is based on designing a library of running gaits that are two-step-periodic. We illustrate the capabilities of the proposed controller through numerical simulations of a five link underactuated robot RABBIT, running over discrete terrain with step lengths that vary between 0.6m and 1.2m. This is about 1.5 times the robot's leg lengths and twice the step length that could have been achieved by walking.

Keywords: Nonlinear system theory, Constrained control, Autonomous robots
Abstract: Controlled invariant set and viability regulation of dynamical control systems have played important roles in many control and coordination applications. In this paper we develop a textit{temporal} viability regulation theory for general dynamical control systems, and in particular for control affine systems. The time-varying viable set is parameterized by time-varying constraint functions, with the aim to regulate a dynamical control system to be invariant in the time-varying viable set so that temporal state-dependent constraints are enforced. We consider both time-varying equality and inequality constraints in defining a temporal viable set. We also present sufficient conditions for the existence of feasible control input for the control affine systems. The developed temporal viability regulation theory is applied to mobile vehicle coordination.

Keywords: Sliding mode control, Decentralized control, Large-scale systems
Abstract: This paper deals with the design of a variable gains super-twisting sliding mode-based decentralized control scheme with application to large-scale modular systems. The system under consideration is composed by a interconnection of so-called active nodes, where the control signal acts, and nonactive nodes, which are not subjected to control actions. A recently proposed scheme to vary the gains for super-twisting architecture is employed to asymptotically achieve reference model-based control, thus ensuring robustness and insensitiveness with respect to uncertainties and disturbances acting on the input channel of each active node. Numerical simulations based on a large-scale mechanical system are provided to assess the proposed control scheme.

Keywords: Sliding mode control, H2/H-infinity methods
Abstract: The super-twisting controller with state dependent disturbance is studied to obtain a preferable parameter set. Whenever the disturbances obey a respective bound, the system will enjoy complete rejection of the disturbance and converge within finite time. However, if such bounds are violated occasionally, then the state will be excited and will converge again after the disturbance obeys the presumed bound. During that violation, the L2-gain from the overall disturbance to the state may be minimized by choosing specific parameters. Our study shows that larger parameters always produce better attenuation for the task of controller design. We propose a lower bound for the controller parameters and study these by simulation.

Keywords: Sliding mode control, Linear systems, Feedback linearization
Abstract: This paper proposes a control method for multi-input linear systems that provides the closed-loop system dynamics of an arbitrary order having a specified feasible a spectrum of poles. By appropriate selection of auxiliary outputs, the system is decoupled into a set of subsystems. The number of these subsystems is equal to the number of control inputs. The desired dynamic of the considered system is achieved using higher order sliding mode, where the sliding mode of appropriate order is realized in each subsystem. The proposed control approach is illustrated by a simulation example.

Keywords: Sliding mode control, Robust adaptive control, Power plants
Abstract: This paper presents an adaptive finite time control scheme using the terminal sliding mode control, for a nonlinear nuclear reactor system. Compared to the linear sliding surface based sliding mode control, terminal sliding mode control with nonlinear sliding surface provides finite time convergence and high precision control. Finite time convergence of error trajectory to the equilibrium point from any initial condition is verified by analysis. Singularity problem associated with the conventional terminal sliding mode control is eliminated by defining an integral terminal sliding surface. An adaptive gain tuning algorithm is integrated with the proposed control law to estimate the upper bound of unknown lumped system uncertainties. Robustness of the closed-loop system is proved with the help of Lyapunov theory. The proposed controller is then applied to a nuclear reactor to track the demand power in finite time. Finally, the effectiveness of the proposed control scheme in the presence of uncertainties and external disturbances is demonstrated through simulation results.

Keywords: Robust control, Sliding mode control
Abstract: Here, we propose the construction of a sliding mode controller(SMC) for the fractional order descriptor (FOD) systems. This controller design mainly aims to stabilize the FOD system even in presence of matched disturbances by rejecting the disturbance. The controller is designed using an integral sliding mode (ISM) technique. Here, the control law is framed in such a way that it is at least continuous, by which it helps in eliminating the harmful chattering effects of the actuator and also meets the admissibility requirement related to control for the FOD systems.

Keywords: Constrained control, Sliding mode control, Optimization
Abstract: The saturated super-twisting algorithm is a sliding-mode control law for robust control in the presence of a saturated control input. It can in principle reject arbitrary bounded and Lipschitz continuous perturbations, and features a control signal that is continuous except for a single jump discontinuity. Its tuning requires the choice of a Lyapunov function from a family of quadratic functions. This choice determines the maximum amplitude of perturbations that are guaranteed to be rejected and the magnitude of the control signal's discontinuity. This contribution proposes a procedure for choosing an optimal Lyapunov function that permits to maximize the available perturbation amplitude and minimize the discontinuity's magnitude.

Keywords: Process control, Sampled data control, Chemical process control
Abstract: This work proposes a dead-time compensator (DTC) with dead-beat disturbance rejection response. The proposed strategy is based on the simplified dead-time compensator (SDTC) because of its simplicity and good performance. A new robustness filter to obtain a dead-beat disturbance rejection response is proposed. The dead-beat robustness filter is then implemented using complex conjugate poles and an additional zero. Simulation results comparing the proposed methodology with the SDTC show that the new obtained controller presents faster disturbance rejection with finite time response.

Keywords: Process control, Large-scale systems, Energy systems
Abstract: Tunnel microwave heating technology has been widely used in grain drying. Development of a reliable control strategy for this process plays an essential role in improving the overall efficiency and productivity. Thus, a tunnel microwave heating system, which can automatically and continuously adjust microwave power and control sample temperature, is designed and used in rice drying. The rice sample is first dried with three fixed microwave power levels to obtain the prior knowledge of tunnel microwave drying process. An expert control system (ECS) based hierarchical control strategy is then developed to regulate the microwave power and track the desired drying temperature. The effectiveness of the proposed control strategy is validated by experiment carried out in the system, which demonstrates that the proposed strategy can commendably control the rice temperature.

Keywords: Process control, Modeling, Model validation
Abstract: Deoiling hydrocyclones are used offshore to reduce the amount of oil in produced water that is discharged to the surrounding marine environment. Stricter regulations and environmental concerns incentify optimization of the hydrocyclones’ deoiling performance. These requirements enforce the pursuit of better models and better controllers. This paper presents suggestions and benchmarking of a modified hydrocyclone grey-box model. The proposed model modifications relax flow conditions, such that the model errors are reduced when the system deviates from normal operation. With the aim of a more realistic model, the cylindrical underflow apex was included in the oil droplet trajectory space. An estimation of the flow that recirculates inside in the hydrocyclone was introduced as this phenomenon has observed in multiple CFD models and research studies. The reduced computational load enabled the proposed model to be executed below 10ms, thus enabling the model to be used for model-based control. The original and the modified model are experimentally validated using a pilot plant. The modified model performs better during all tests and provides a reasonable prediction of deoiling separation efficiency compared with the separation efficiency obtained through real-time oil-in-water measurements.

Keywords: Process control, Adaptive control, Switched systems
Abstract: This paper presents a new type of adaptive binary controller that generates a continuous actuator signal from a binary input signal. Applications for such a controller arise, for example, when an actuator already exists (such as a heater in an HVAC system or an inverter driven motor in a hydraulic system) but a sensor must be retrofitted and therefore kept as cheap as possible (such as a bimetal switch for a target temperature or a floating switch for a target level). We claim the proposed adaptive controller is an improvement over a simple on/off controller in several aspects. It reduces the input amplitude to a value as low as possible to minimize the stress on the actuator, while still being able to react to disturbances. We investigate basic stability properties for the proposed controller in a class of relay feedback systems by analyzing resulting limit cycles and Poincar´e maps. All theoretical results are corroborated with a hydraulic laboratory setup.

Keywords: Process control
Abstract: This work proposes a dead-time compensator (DTC) with feedforward action for first-order plus dead-time processes (FOPDT) with measurable disturbance. The proposed controller structure is based on both feedforward Smith Predictor and the Simplified Dead-time Compensator (SDTC). Simulation results compared with other recent literature propositions show the effectiveness of the proposed controller.

Keywords: Process control
Abstract: This paper presents a Model Predictive Control (MPC) strategy based on Linear Parameter Varying (LPV) models with varying delays affecting states and inputs. The proposed control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. Moreover, the solution of the optimization problem associated with the MPC design is achieved by solving a series of Quadratic Programming (QP) problem at each time instant. This iterative approach reduces the computational burden compared to the solution of a non-linear optimization problem. A pasteurization plant system is used as a case study to demonstrate the effectiveness of the proposed approach.

Keywords: Traffic control, Observers for nonlinear systems, Predictive control for nonlinear systems
Abstract: Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume perfect knowledge of the accumulation state and inflow demands, both requiring information about the origins and destinations of drivers. Such assumptions are problematic for practice due to measurement noise and difficulty of obtaining OD-based information. We address these by a nonlinear moving horizon estimation (MHE) scheme for the combined demand and state estimation for a two region large-scale urban road network with dynamics described via macroscopic fundamental diagram. We consider various measurement configurations likely to be encountered in practice, such as measurements on regional accumulations and transfer flows without OD information, and provide results of their observability tests. A model predictive perimeter control scheme is combined with the MHE to present an application case. Simulation studies demonstrate operation of the proposed scheme.

Keywords: Traffic control, Robust control
Abstract: Many traffic control strategies in the literature are designed based on various traffic flow models, which do not include parameter uncertainties in their intersection queuing dynamics. Hence, these control strategies are unable to handle conditions when the assumed values for the parameters vary significantly from their real values. Moreover, many utilized state feedback control approaches to develop the traffic signal controllers lack the treatment of control and state constraints directly in the design phase.

This paper considers robust traffic signal control for uncertain urban road networks. First, parameter uncertainties are integrated into the store-and-forward (SF) model, which is utilized in this paper to describe the queuing dynamics for traffic signalized intersections. Then, the uncertain SF model is utilized to design a robust feedback controller by an interpolation-based approach. This approach (i) guarantees robustness against all parameter uncertainties, (ii) handle control and state constraints, and (iii) present a computationally cheap solution. Finally, numerical results for an isolated signalized intersection show a comparison between the developed interpolating controllers and other controllers in the literature. The results demonstrate the performance advantages from applying the robust interpolating controllers.

Keywords: Traffic control, Transportation systems
Abstract: This paper introduces a new concept for traffic control in freeway systems by considering the presence of vehicle platoons as possible actuators to mitigate traffic congestion. It is known that the formation of platoons presents relevant advantages for the involved vehicles, first of all in terms of fuel consumption. Besides this, in this paper, the concept of platoon is further developed and it is investigated if the presence of platoons in the traffic stream can be exploited by a traffic controller to reduce traffic congestion. In this sense, each platoon can be seen as a moving bottleneck which influences the surrounding traffic conditions. The dimension of the platoon (i.e. number of vehicles composing it) and its velocity can be seen as control variables of a traffic management tool. In this paper, only the platoon speed is considered as control variable. Feedback regulators are discussed and their effectiveness is analyzed in simulation.

Keywords: Modeling, Large-scale systems
Abstract: This paper presents a new planning and decision-making method in large scale traffic networks for predicting how traffic evolves in special events, emergencies and changes in the city mobility demands. The proposed method is based on a 2-D aggregated traffic model for large scale traffic networks which describes traffic evolution as a fluid in two space dimensions. We propose an extension of the model by including additional state density variables, each one associated to a particular layer describing vehicles evolving in different directions. The model is a 2D-PDE described by a system of conservation laws. For this specific case, the resulting PDE is not anymore hyperbolic as typically the LWR model but results in a hybrid hyperbolic-elliptic PDE depending on the density level. In this case, usual numerical schemes may be not valid and often lead to oscillation in the solution. Thus, we consider a high order numerical scheme to improve the numerical solution. Finally, the model is used to predict how the typical traffic evolution will be impacted in particular scenarios like special events or changes in demands. Comparative simulations are provided.

Keywords: Transportation systems, Traffic control, Autonomous systems
Abstract: Truck platooning, where vehicles drive close together in order to reduce air drag, is a promising technology that is slowly reaching maturity. In order to fully exploit its potential, we will need to be able to efficiently form platoons en-route, while driving on the road, by having the vehicles adjust their speeds so that they catch-up and merge into a platoon. Since this means that the participating vehicles will have to deviate from their own optimal speed profiles, experiencing unpredicted disturbances caused by the surrounding traffic might lead to higher fuel consumption. Therefore, in order to increase the fuel savings and improve their predictability, we devise a control strategy that takes into account the traffic conditions and yields optimal catch-up speeds for both the leader and the follower vehicle. The control strategy is based on minimizing total energy consumption, and it models the vehicles as moving bottlenecks. We compare this strategy to one that does not consider the influence of traffic, and show that it achieves better results in terms of energy savings.

Keywords: Traffic control, Transportation systems, Optimal control
Abstract: When inbound and outbound traffic on a bi-directional motorway is unbalanced throughout the day a lane management strategy called tidal (reversible) flow lane control is usually applied. In this control case, the direction of one or more contraflow buffer lanes is reversed according to the needs of each direction. This paper introduces a basic dynamical model for tidal traffic flow and considers the minimum travel-time, minimum-time, and maximum throughput optimal control problems for efficient motorway tidal flow lane control. Lane management is effectuated by a control variable, indicating the number of lanes opened or closed in each direction of traffic. To derive the analytical form of optimal control, the Pontryagin's maximum principle is employed. The obtained optimal control is intuitively natural of bang-bang type, as also shown in a previous work by the authors [1]. It takes only the values ±1 and switches between these values at most once. In other words, the optimal control strategy consists of switching between opening and closing in each direction of traffic one contraflow buffer lane. Of course it is an open-loop control, and thus the switch time (if applicable) depends on the initial conditions. In the case of the maximum throughput optimal control problem, semi-state feedback control is obtained and singular arcs might exist. Finally, cumulative arrival rate and output curves for both directions of traffic are used to provide a graphical interpretation of the minimum travel-time optimal control problem and obtained bang-bang control.

Keywords: Stability of hybrid systems, Lyapunov methods, Hybrid systems
Abstract: This paper studies an input-to-state stability (ISS) property for nonlinear control system with impulses at fixed moments. We propose a novel Lyapunov-based theorem ensuring the ISS that is less restrictive compared to the existing results in the literature. Sufficient conditions for ISS are formulated in terms of a non-exponential candidate ISS-Lyapunov function with nonlinear rates equipped with an average-type dwell-time condition. The benefits of the proposed approach are discussed and illustrated on an example. To the best of authors' knowledge, this is the first result that combines the advantages of nonlinear rates of the corresponding ISS-Lyapunov function and an average dwell-time for the ISS analysis of impulsive systems. The proposed technique of the proof, together with a new dwell-time condition, enables relaxing the existing sufficient conditions for the ISS for different classes of impulsive control systems (e.g. stochastic, infinite-dimensional) that employ a fixed dwell-time.

Keywords: Stability of hybrid systems, Switched systems, Nonlinear system theory
Abstract: A spatial (3D) piecewise linear system with multiple modes having conewise state spaces is considered. A single mode in this system is called transitive from one (respectively, two) of its borders if every trajectory that starts in its interior or at a border travels in its interior, hits that border (respectively, one of the two borders), and goes out of the cone. This paper characterizes transitive cones in case the dynamics in the cone is dictated by real and distinct eigenvalues. An example of a 3D piecewise linear system composed of transitive cones illustrates how a nonlinear oscillator can be synthesized.

Keywords: Switched systems, Fuzzy systems, Stability of nonlinear systems
Abstract: Relaxed conditions for switched Takagi-Sugeno fuzzy systems under state feedback are proposed. The result explores two strategies to decrease the linear matrix inequalities (LMIs) conservatism. Properties of membership are explored to add slack matrix variables into the LMI formulation and a standard right hand side LMI relaxation procedure is extended for switched TS fuzzy systems. Numerical examples illustrate the efficiency of the presented results.

Keywords: Quantized systems, Linear systems, Optimization
Abstract: The optimal dynamic quantizer, supposed to be combined with conventional feedback controller, is a model-based converter from a continuous-valued control input to a discrete-valued one that minimizes the resulting quantization error. A known technical difficulty was the case of non-minimum phase systems that it tends to result in unstable dynamic quantizer due to its model-based nature. In this paper, we propose a method to design a stable dynamic quantizer for multi-input, multi-output (MIMO) non-minimum phase systems. The key technique is an appropriate serial system decomposition, likewise the so-called inner-outer factorization strategy, to separate the system into the minimum phase factor and the non-minimum phase one. We discuss some theoretical issues concerning this approach and show that the optimal dynamic quantizer is only dependent on the outer part.

Keywords: Quantized systems, Signal processing, Biological systems
Abstract: A new method of curve form based quantization of short time series data is proposed here for the modeling and analysis of the dynamics in biological systems. We use a smoothing spline to fit our data with predefined qualitative curve forms as constraints firstly. Then we determine the significant curve form based on the generalized cross validation. After that, we calculate a pattern fit to quantize data according to the curve form. The proposed method selects the number of quantization stages automatically and reduces the influence of noise. Finally, examples are given to illustrate the proposed approaches.

Keywords: Quantized systems, Stability of nonlinear systems, Uncertain systems
Abstract: In this paper the problem of prescribed tracking performance for a class of nonlinear systems in the presence of uncertain dynamics and state quantization is addressed. The proposed controller is capable of not only establishing stability for the closed-loop system but also guaranteeing predefined performance bounds on the transient and steady-state error performance. The control design procedure incorporates only quantized measurements, is model-free and of low-complexity, with respect to the specifications of the quantizer employed for the output measurement. For the quantization, static uniform hysteretic quantizers are implemented, to simplify further the control solution, and guarantee the well-posedness of the closed-loop and the chattering-free quantization performance. Simulation studies clarify all design aspects and illustrate its effectiveness.

Keywords: Optimal control, Autonomous systems, Optimization algorithms
Abstract: In this paper, we provide a new approach to analyze backward reachable sets for nonlinear dynamical systems, which are formulated as constrained optimal control problems. This class of problems is related to characterization of the level set of the value function by solving the associated Hamilton-Jacobi-Bellman (HJB) equation, a first-order nonlinear PDE. However, in many cases, solving HJB equations numerically requires high performance computation. Instead of solving HJB equations, we use the pseudospectral Legendre method to reformulate backward reachability problems as nonlinear programs (NLPs). The backward reachability problem is discretized by state and control variables parameterized at Legendre-Gauss-Lobatto collocation points using the pseudospectral method. We convert several reachability examples to the corresponding NLPs, and characterize explicit backward reachable sets using the gradient projection method.

Keywords: Optimal control, Optimization
Abstract: In this paper, we investigate a sparse optimal control in a feedback framework. We adopt the dynamic programming approach and analyze the optimal control via the value function. Due to the non-smoothness of the L0 cost functional, in general, the value function is not differentiable in the domain. Then, we characterize the value function as a viscosity solution to the associated Hamilton-Jacobi-Bellman (HJB) equation. Based on the result, we derive a sufficient and necessary condition for the L0 optimality, which immediately gives the optimal feedback map. In addition, we consider the relationship with L1 optimal control problem and show an equivalence theorem.

Keywords: Optimal control, Linear systems
Abstract: In this paper the problem of optimizing the regulation of a weakly dual redundant plant with multiple actuators is addressed. When the system is underactuated, only a subset of the outputs can be independently controlled. In this regard, the main objective of the paper pertains the optimization of the steady-state performance of the plant. A connection between the overall optimal input and the inputs that provide a perfect reference tracking of the full controllable square subsystems is established. The proposed control scheme is validated by the simulation experiment of a tracking problem for a three mass coupled system.

Keywords: Optimal control, Constrained control, Optimization algorithms
Abstract: In this paper, we address the problem of finitehorizon distribution steering of state-affine nonlinear systems using open-loop control. The terminal distribution is constrained by an isotropy constraint which enforces uniformity in all directions. With the nonlinear dynamics and the isotropy constraint, a constrained nonlinear optimization problem is formulated. To solve this nonlinear problem, this paper proposes a sequential linearized algorithm that generates feasible iterates. The main advantage of the proposed algorithm is that it requires no a priori knowledge of the global Lipschtiz property of the gradients of the cost and constraint functions. The performance of the proposed algorithm is demonstrated by a beamline calibration problem in proton therapy.

Keywords: Optimal control, Optimization algorithms, Maritime
Abstract: This work concerns a specific indirect method to solve an optimal control problem with state constraints in which the dynamics are driven by an ordinary differential equation involving a three-dimensional steady flow field. A moving object within this field is considered subject to a path constraint given by a cylinder. The given control system is linear, but the vector flow field may exhibit essential nonlinearity. An indirect numerical method is proposed based on the maximum principle in Gamkrelidze’s form. This form of optimality conditions uses the concept of the extended Hamilton-Pontryagin function. The extended Hamilton-Pontryagin function differs from the conventional one in view of an additional term associated with the measure Lagrange multiplier. The proposed computational method essentially relies on the continuity of the measure multiplier, which is guaranteed under certain regularity conditions. Moreover, this property, together with the regularity condition, allows us to obtain an explicit expression for the measure multiplier. The application of the maximum principle yields a two-point boundary-value problem, which is solved by a variant of the shooting method.

Keywords: Optimal control, Robotics
Abstract: While Approximate Dynamic Programming has successfully been used in many applications involving discrete states and inputs such as playing the games of Tetris or chess, it has not been used in many continuous state and input space applications. In this paper, we combine Approximate Dynamic Programming techniques and apply them to the continuous, non-linear and high dimensional dynamics of a quadcopter vehicle. We use a polynomial approximation of the dynamics and sum-of-squares programming techniques to compute a family of polynomial value function approximations for different tuning parameters. The resulting approximations to the optimal value function are combined in a point-wise maximum approach, which is used to compute the online policy. The success of the method is demonstrated in both simulations and experiments on a quadcopter. The control performance is compared to a linear time-varying Model Predictive Controller (MPC). The two methods are then combined to keep the computational benefits of a short horizon MPC and the long term performance benefits of the Approximate Dynamic Programming value function as the terminal cost.

Keywords: Iterative learning control, Linear systems, Robust control
Abstract: This paper addresses the design of a robust iterative learning control (ILC) scheme for a class of linear systems with time-varying uncertainties and non-repetitive disturbances. The proposed design method modifies two-dimensional/repetitive setting to include the requirement for Hinf disturbance attenuation. Also, it is shown that the conversion of the control problem to one of stability along the trial for a linear repetitive process leads to design based on linear matrix inequality (LMI) computations. In particular, sufficient conditions for the existence of a robust ILC updating law are derived together with the design algorithms for the associated controller matrices. Obtained results show that the proposed control law is able to fulfil the imposed requirements, i.e., they are suitable for the systems with time-varying uncertainties as well as non-repetitive disturbances. An illustrative example is given to highlight the principles, effectiveness and possible applicability of the proposed method.

Keywords: Iterative learning control, Stochastic systems, Lyapunov methods
Abstract: This paper develops a new iterative learning control law for stochastic linear systems. The design is based on recently developed passivity based control of repetitive process, which are a class 2D nonlinear systems. In particular, this theory is used to design a nonlinear control law for linear dynamics, which allows tuning of the control law to achieve better performance. A supporting example is given, including a comparison with a previously reported design.

Keywords: Stability of linear systems, Linear systems, Discrete event systems
Abstract: A simple and straightforward sufficient and necessary condition is given to check the asymptotic stability of 2D scalar Fornasini-Marchesini models. The condition only looks at the absolute value of the constants of the model and hence make it very practical to derive simple examples. A possible extension to the matrix case is discussed notably when the matrices commute.

Keywords: Stability of linear systems
Abstract: This paper proposes a new LMI condition for the structural stability of 2D discrete Roesser models. The condition is necessary and sufficient. Unlike previous ones, it does not involve a hierarchy level. More precisely, if many of the LMI conditions encountered in the literature are dependent on a hierarchy level which has to tend to infinity or at least to be very large to hope for necessity, the here-presented test is based upon a straightforwardly expressed LMI system in which no hierarchy level has to be guessed.

Keywords: Modeling, Distributed parameter systems, Biological systems
Abstract: The behavior of a distributed parameter system can be represented by an expansion into eigenfunctions. It allows to calculate the output signal in dependence on the parameters of the system. This contribution considers the inverse problem: Estimate the system parameters from noisy measurements of the output. To this end, an eigenfunction expansion serves to establish a state-space description of the distributed parameter system. The corresponding state-space matrices define an extended Kalman filter to perform estimation and tracking of parameter values. This approach is shown here for a diffusion problem in one spatial dimension. The value of the diffusion parameter is estimated from a simulated particle concentration under varying conditions. Problems of this kind arises for example in the emerging field of molecular communications.

Keywords: Biomedical systems, Iterative learning control
Abstract: In clinical practice left ventricular assist devices are usually driven at constant speed and thus not adaptive to changes in hemodynamic conditions. In this paper, a norm-optimal iterative learning control algorithm with an economically motivated cost function is developed, which aims at keeping ventricular volume and pressure in a predefined valid range instead of tracking specific setpoints. The algorithm is tested in silico to changes in preload and afterload. Both experiments represent naturally occurring types of parameter variations. A comparative assessment is performed between the proposed control algorithm and a constant speed controller as well as a norm-optimal iterative learning control algorithm, which is designed to shape the end-diastolic volume. The results confirm that the algorithm is able to keep ventricular volume and pressure in predefined ranges to prevent e.g. ventricular suction and dilatation. Furthermore, it offers the physician the opportunity to implement different training protocols by adapting the economic cost function.

Keywords: Linear systems, Computer aided control design
Abstract: This study is concerned with the H2 state-feedback controller synthesis problem under positivity constraint on the closed-loop system. This problem is believed to be a nonconvex problem in both continuous- and discrete-time system settings and hence remains to be the most challenging issue in positive system theory. For this hard problem, in the discrete-time system setting, the authors recently proposed a technique for the lower bound computation of the best achievable H2 performance by a specific treatment of finite impulse responses (FIRs) of the closed-loop systems. The goal of this paper is to extend this idea to the continuous-time system setting. Even though there is no notion of FIR in (finite-dimensional) continuous-time system impulse responses, the truncation of the Taylor series expansion of the matrix exponential function in the impulse response paves the way for obtaining a semidefinite programming problem (SDP) for the lower bound computation. We show that, by increasing the truncation degree, we can construct a sequence of SDPs that generates a monotonically non-decreasing sequence of the lower bounds. By combining this lower bound computation technique with heuristic upper bound and suboptimal gain computation techniques, it becomes possible to draw definite conclusion on the quality of the computed suboptimal gains.

Keywords: Linear systems, Constrained control, Stability of linear systems
Abstract: In this paper, we consider the computation of the maximal invariant set of linear systems with a class of nonlinear constraints that has quadratic relaxations. With these quadratic relaxations, we are able to determine a sufficient condition for the maximal invariant set. Using the sufficient condition, a new algorithm is presented by solving a set of linear matrix inequalities. Under mild assumptions, the proposed algorithm will terminate in finite time. The performance of this algorithm is demonstrated on several numerical examples.

Keywords: Linear systems, Control education
Abstract: The Morse normal form for linear systems (or the Kronecker normal form for singular matrix pencils) is a deep result and a useful tool of linear system theory. This paper presents an alternative proof of this advanced result that makes use of the Smith normal form over the ring of proper rational functions. The relationship of these two classical normal forms is of interest as is the proof itself, which is entirely constructive and describes the sequence of operations needed to determine the Kronecker/Morse normal form.

Keywords: Linear systems
Abstract: This paper provides a system theoretic investigation of the building blocks of the supremal output-nulling, reachability and stabilizability subspaces that are obtained from the eigenstructure assignment approach based on projecting the bases of the null-spaces of the Rosenbrock matrix pencil on the state space. In particular, we show that the use of a certain set of eigenvalues to build such bases guarantees that their span is the largest output-nulling subspace of the system such that the closed-loop mapping restricted to it has exactly these eigenvalues, and that all the output-nulling subspaces constructed in this way share the same reachability subspace. This paper also casts some light onto the fact that, even if these building blocks depend on the values of the indeterminate used to compute the kernels of the Rosenbrock matrix, the dimension of their sums for different values is independent of the specific values of the indeterminate.

Keywords: Linear systems
Abstract: This paper deals with the problem of designing a state-feedback plus feedforward controller for multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems to monotonically track constant step references at any desired rate of convergence for arbitrary initial conditions. We show in particular how the solution of this problem can be generalized whenever the full global monotonic tracking is not achievable, but monotonicity is only required for some specific output components.

Keywords: Linear systems, Nonlinear system theory, Nonlinear system identification
Abstract: In this paper, an analytical procedure to derive the efficiency of linear and nonlinear physical systems is presented. This procedure allows to compute the efficiency map both on the plane of the input power variables and on the plane of the output power variables. Additionally, the paper highlights the parameters to be adjusted in order to enlarge the high-efficiency region of the system. The presented procedure can also be used in conjunction with a least square algorithm in order to estimate the unknown parameters of the considered physical system. The effectiveness of the procedure has been tested in Matlab/Simulink to estimate the parameters of an actual PMSM electric motor. The obtained results show a very good matching between the actual and the estimated efficiency maps.

Keywords: Flexible structures, Optimal control, Predictive control for nonlinear systems
Abstract: A nonlinear modal-based reduced-order model, equipped with an efficient adjoint-sensitivity analysis, is presented as a low computational cost framework for optimal control of very flexible structures, with particular focus on efficiently computing finite rotations. Multiple shooting is shown to improve convergence of a highly nonlinear problem when compared to the single shooting case, with optimisation further accelerated via parallelisation, which suggests the presented approach may be employed for real-time control of very flexible structures.

Keywords: Manufacturing processes, Control of metal processing, Mechatronics
Abstract: Abstract— Laser metal deposition, also known as laser cladding or direct metal deposition, is an emerging additive manufacturing technology for the creation of solid 3D-parts. We propose a feedforward controller for the melt pool width that is derived from a first principle model based on the heat conduction equation. Essentially, it adjusts the laser power to the path taken by the machine, thus using another input that is usually kept constant during a production process. The controller is constructed for compatibility with existing computerized numerical (CNC) control for laser metal deposition. This is accomplished by designing the feedforward controller for compatibility with G-code, the standard programming language for CNC machines. Since G-code is not based on time steps or a sampling frequency but, roughly speaking, on velocity and position signals, special care must be taken to ensure compatibility of the proposed model-based discrete-time feedforward controller and the existing CNC machine control. The presented controller is evaluated with a benchmark scenario, where it shows good performance without the need for any additional sensors.

Keywords: Metabolic systems, Biomedical systems, Modeling
Abstract: To test newly developed insulin dosing algorithms for type 1 diabetes (T1D) patients, researchers and engineers often use simulation studies with complex physiological models of the human glucose metabolism. However, since those models typically do not include any time-variant behavior, simulation studies can easily lead to an overestimation of control performance. In order to have an estimate of the effect of intrapatient variability on glucose levels, so called "Deviation Analyses" have recently been proposed. These combine real recorded data as a baseline with models of insulin action in order to predict the effect of dosing algorithms. Therefore, they include the intrapatient variability of the recorded glucose traces. As an alternative, the information about intrapatient variability can be stored in a parameter interval of so-called interval models and can be used to compute the envelope of possible glucose trajectories. The current paper proposes to use Deviation Analysis and interval models as complementary tools to enhance the performance assessment of insulin dosing algorithms under intrapatient variability: Interval models can be used to detect unrealistic Deviation Analysis trajectories or, alternatively, unconservative and overly conservative interval models can be searched for using Deviation Analysis results.

Keywords: Modeling, Behavioural systems, Reduced order modeling
Abstract: In this paper, we present the dynamics of the simple pendulum by using the fractional-order derivatives. Equations of motion are proposed for cases without input and external forcing. We use the fractional-order Euler-Lagrange equations to obtain the fractional-order dynamic equation of the simple pendulum. We perform equilibria analysis, indicate the conditions where stability dynamics can be observed for both integer and fractional-order models. Finally, phase diagrams have been plotted to visualize the effect of the fractional-order derivatives.

Keywords: Modeling, Energy systems
Abstract: Liquid cooling systems have better heat dissipation capabilities than air based ones, and are expected to become a standard choice in future data centers, due to the ever increasing power density and heat rejection needs of the compute infrastructure. A convenient side-effect of implementing liquid cooling is that it facilitates the efficient recovery of the heat waste. However designing and managing these heat recovery infrastructures benefit from having control-oriented models that can accurately describe how different operating conditions of the to-be-cooled heat sources will affect the thermal status of the coolant. The aim of this manuscript is to derive control-oriented models of liquid immersion cooling systems, i.e., systems where the compute infrastructure is immersed in a vessel filled with dielectric fluid. More specifically we derive, starting from physical interpretations, a general lumped-parameters gray box dynamical model that has - as inputs - the electrical consumption of the heat sources and the working point of the heat recovery system, and has - as outputs - the temperature distribution of the coolant in the most relevant points of the system. Beyond proposing this modelling methodology we also validate the generalization capabilities of the obtainable models. In specific, we test the achievable statistical performances in a field case, plus compare with the ones of classical black box system identification strategies. We thus report that in the considered field case our gray box model reached a fit index of 91.08% when simulating test sets, while the best black box model we have been able to identify reached (on the same test sets) fit indexes of only 72.56%.

Keywords: Modeling, Optimization, Transportation systems
Abstract: Controlling food quality and reducing waste is one of the most challenging tasks in the food industry, as it is facing high rates of wastage, leading to negative environmental impact. This research focuses on improving the scheduling and control of the supply chain of Irish lamb meat using real-time quality and temperature information. Temperature controlled reefers and sensor technologies can be used to monitor and set the temperature during transport and storage. In order to minimize waste, while at the same time optimizing cooling and transport costs, a mathematical model is proposed. The model consists of two aspects. The quality aspect considers the shelf life of Irish lamb which is related to temperature. The logistic aspect considers supply chain scheduling. With this model, a strategy is proposed to determine the movements of meat, as well as the temperature setting of cooling equipment. Results of simulation experiments indicate a sustainable approach can reduce or even eliminate waste and decrease operational costs when real-time monitoring and control is used.

Keywords: Fault detection and identification, Linear time-varying systems
Abstract: Partial least-squares techniques have been widely used for quality-related fault detection purposes when there is a linear relation between process variables and quality-outputs. However, those methods are suitable when the process variables and quality-outputs have constant mean values. In this paper, a novel least-squares-based scheme is proposed for quality-related fault detection of the dynamic linear processes, in which the measured variables and quality-outputs may have time-variant mean values. The proposed method is built upon a cascade modeling framework including complete orthogonal projection of the process variables onto quality-related and quality-unrelated subspaces and finding the principal manifolds for the projected components which represent their underlying auto-regressive moving average models. Moreover, a new residual index is proposed, which is insensitive to the mean changes of the process variables. The proposed method and dynamic improved partial least-squares (DIPLS cite{DIPLS}) technique are finally applied to a numerical case study and a non-isothermal continuous stirred tanks reactor (CSTR) benchmark, and the simulation results demonstrate the effectiveness of the proposed scheme.

Keywords: Fault detection and identification, Optimization, Model validation
Abstract: In this paper, we propose a partition-based parametric active model discrimination approach that distinguishes among a set of discrete-time affine time-invariant models with uncontrolled inputs, model-independent parameters that are revealed in real-time and noise. By partitioning the operating region of the parameters, the problem turns into a sequence of offline optimization problems. Thus, at each time instant, we only need to determine which subregion in the resulting partition tree the revealed parameters lie in and select the corresponding pre-computed inputs. The offline optimal input design problem is formulated as a bilevel problem and further cast as a mixed-integer linear program (MILP). Finally, we demonstrate the effectiveness of the proposed approach for estimating driver intention in a lane-changing scenario.

Keywords: Fault diagnosis, Optimization, Uncertain systems
Abstract: The main objective of active fault diagnosis is the design of separating input signals that enhance the detection and isolation of faults in modern technological systems. A major consideration when evaluating active fault diagnosis methods is robustness in the presence of modeling uncertainties. The presence of modeling inaccuracies will typically compromise the performance of the separating input signals designed for effective fault diagnosability. This work investigates a distributionally robust active fault diagnosis approach for nonlinear systems, which takes into consideration variation or ambiguity in the uncertain parameters of the models. A probabilistic approach is presented using total variation distance as an information constraint, and as a measure for the separation of multiple models based on the similarity of their output probability density functions. The effectiveness of the proposed approach is demonstrated through an application to a three-tank system.

Keywords: Fault tolerant systems, Fault estimation, Robust control
Abstract: In this paper we present a Disturbance Observer Based Control for an octarotor unmanned aerial vehicle, taking into account the actuator dynamics. The scheme is robust to the uncertainties of the model dynamics, the actuator dynamics and it can also manage the presence of actuator faults and failures. The control law is based on a combination of Back- stepping and Dynamic Surface Control, where the feedback linearisation change of coordinates is applied to part of the state space dynamics in order to avoid the explosion of terms. The distribution of the control efforts among the actuators is optimally provided by an allocation algorithm which minimizes the energy consumption even in presence of uncertainties.

Keywords: Fault accommodation, Cooperative control, Robust adaptive control
Abstract: This paper investigates the Fault Tolerant Control (FTC) for Multi-User Telerehabilitation Systems (MUTSs) which are modelled as Ito stochastic differential equations with the occurrence of actuator loss-of-effectiveness faults, directed link failures, communication noise, and disturbances. Furthermore, an active FTC strategy using an Adaptive Sliding Mode Control (ASMC) method is addressed. By employing such strategy, MUTSs achieve stochastic consensus in the mean square sense onto the predefined stochastic switching sliding surfaces in finite time. Finally, Stochastic Input-to-State Stability (SISS) of the systems' states (positions, velocities, and orientations) will be shown and a numerical simulation will be provided.

Keywords: Fault detection and identification, Identification, Machine learning
Abstract: Bearings are one of the most common components in automatic machines. Diagnosis and prognosis of their working condition is crucial for minimization of downtime and maintenance costs. Different approaches may be adopted to either solve or mitigate the problem of identifying incipient faults during machinery operations. In this paper, we propose a simple and efficient yet effective method to solve this problem by exploiting the edge-computing capabilities of PLCs. Accelerometer signals are modeled as AR processes whose coefficient are used as features for machine learning, based on logistic regression algorithm, to perform Fault Detection and Isolation (FDI). Estimation and prediction are both implementable on-board the PLC, while machine learning can be carried out remotely, in a cloud computing perspective. The exploitation of AR modelling gives a simple and inherent methodology for feature selection. We apply the procedure to the Case Western Reserve University database, a widely known and used benchmark, to highlight its performance with respect to similar fault recognition techniques.

Keywords: Energy systems, Machine learning, Neural networks
Abstract: In this paper, we propose a day-ahead strategic marketing method for multi-period energy markets using a machine learning approach based on neural networks. An aggregator, which has renewable energy generation devices, needs to schedule the energy production and consumption (prosumption) in a situation where the renewable power generation amount is not exactly predicted in day-ahead scheduling. If imbalance, defined as the difference between a day-ahead schedule and an actual prosumption profile, occurs, the aggregator is required to pay imbalance penalty costs. As a scheduling method to avoid paying imbalance penalty costs, we propose a scheduling model by machine learning based on the results of past transactions. In particular, the scheduling model is given as a neural network, which has an advantage in terms of computational costs compared to the kernel method. For developing a training algorithm, we show that the gradient of the profit function with respect to design parameters can be calculated as a solution to linear programming. Finally, we show the efficiency of the proposed method through a numerical example.

Keywords: Adaptive control, UAV's, Robotics
Abstract: This paper presents the nonlinear adaptive control of a quadrotor endowed with a 2 degrees of freedom (DOF) manipulator. By considering the quadrotor and the robot arm as a combined system, complete modeling of the aerial manipulation system (AMS) has been presented using the Euler-Lagrange method. A hierarchical nonlinear control scheme which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias and the masses have been tackled along with the reaction forces acting on the attitude part of the AMS. The proposed technique has been validated through simulations in two different scenarios.

Keywords: Adaptive control
Abstract: In this paper, we study continuous time adaptive extremum localization of an arbitrary quadratic function F(·) based on Hessian estimation, using the measured signal intensity by a sensory agent. The function F(·) represents a signal field with a source located at the maximum point of F(·). A gradient based adaptive Hessian parameter estimation and extremum localization scheme is developed considering a linear parametric model of field variations. Stability and convergence analysis of the proposed scheme is provided, establishing asymptotic convergence of the Hessian parameter and location estimates to their true values robustly to drift in the extremum location. Simulation test results are displayed to verify the established properties of the proposed scheme as well as robustness to signal measurement noise.

Keywords: Adaptive systems, Aerospace, Machine learning
Abstract: A condition-based control framework is proposed for gas turbine engines using reinforcement learning and adaptive dynamic programming (RL-ADP). The system behaviour, specifically the fuel efficiency function and constraints, exhibit unknown degradation patterns which vary from engine to engine. Due to these variations, accurate system models to describe the true system states over the life of the engines are difficult to obtain. Consequently, model-based control techniques are unable to fully compensate for the effects of the variations on the system performance. The proposed RL-ADP control framework is based on Q-learning and uses measurements of desired performance quantities as reward signals to learn and adapt the system efficiency maps. This is achieved without knowledge of the system variation or degradation dynamics, thus providing a through life adaptation strategy that delivers improved system performance. In order to overcome the long standing difficulties associated with the application of adaptive techniques in a safety critical setting, a dual-control loop structure is proposed in the implementation of the RL-ADP scheme. The overall control framework maintain guarantees on the main thrust control loop whilst extracting improved performance as the engine degrades by tuning sets of variable geometry components in the RL-ADP control loop. Simulation results on representative engine data sets demonstrate the effectiveness of this approach as compared to an industry standard gain scheduling.

Keywords: Machine learning, Adaptive control, Energy systems
Abstract: Harvesting energy from the environment, e. g.ocean waves, is a key capability for the long-term operation of remote electronic systems where standard energy supply is not available. Rotating pendulums can be used as energy converters when excited close to their eigenfrequency. However, to ensure robust operation of the harvester, the energy of the dynamic system has to be controlled. In this study, we deploy a light-weight reinforcement learning algorithm to drive the energy of an Acrobot pendulum towards a desired value. We analyze the algorithm in an extensive series of simulations. Moreover,we explore the real world application of our energy-based reinforcement learning algorithm using a computationally con-strained hardware setup based on low-cost components, such as the Raspberry Pi platform.

Keywords: Iterative learning control, Intelligent systems, Adaptive systems
Abstract: In this paper, iterative learning control (ILC) is proposed as an alternative approach that can be used to simplify the design of linear controllers for rejecting repetitive disturbances affecting a system. Since ILC is a data-driven method, its usage allows rejecting repetitive disturbances without a priori knowledge of their nature. The benefits of ILC appear especially when a system is subject to complex repetitive disturbances. This is simply because it would require a control designer to spend much more efforts to obtain similar results applying a state augmentation by building models for the same disturbances (Internal-Model-Principle based control). Accordingly, this paper first shows the equivalence of performing state augmentation and applying ILC in case of a simple sinusoidal repetitive disturbance. Next, a workflow named Learning Based Controller Tuning (LBCT) is proposed to simplify the parameter tuning of linear controllers under repetitive disturbances. The feasibility of LBCT is analysed by testing the ILC on a system subject to complex repetitive disturbances in two different forms: linearly combined sinusoidal signals and non-linearly combined sinusoidal signals. The results demonstrate that ILC can successfully learn the required controller parameters to reach a good rejection performance against the repetitive disturbances without a need of modelling them. This supports the fact that the tuning of linear controllers may be automated by integrating an ILC to the system.

Keywords: Aerospace, Modeling, Optimization algorithms
Abstract: This paper considers the application of Particle Swarm Optimization (PSO) to decentralized control of a Control Moment Gyroscope (CMG). The PSO is used to obtain optimized gains for a decentralized PID controller. A cost function with ITAE index combined with the control signal variance is proposed for the system. A simple velocity feedback is proposed for damping resonant poles prior to the decentralized control. Simulation and practical results are presented in order to validate the methodology.

Keywords: Agents and autonomous systems, Cooperative control, Distributed cooperative control over networks
Abstract: In this paper, we propose a new concept of a rigidity, i.e. generalized rigidity: a general version over the distance rigidity [1]–[3], bearing rigidity [4]–[6] and weak rigidity [7]–[10]. In contrast with the distance rigidity, bearing rigidity and weak rigidity, the new concept makes use of all of distance, bearing and angle constraints to characterize a unique formation shape. For its application, in the 2- and 3-dimensional spaces, we apply the new concept to formation control problems under a gradient-based control law. Then we analyze the stability of the problems, and consequently provide locally asymptotic convergence of a n-agent formation to a desired formation. Finally, we provide a numerical simulation on the control problems to show validation on our theories.

Keywords: Agents and autonomous systems, Flexible structures, Transportation systems
Abstract: Research related to electric vehicles (EV) is mainly focused on the hardware such as battery charging method, and still there is a lack of software research such as billing system that needs to be developed realistically. The result of charge measured in the charging EV can be different from the charged amount claimed to be charged in the charging system (CS). However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviours of EVs with distinct energy delivery preferences of the CSs. In this paper, a novel EV participation charging scheme is proposed for a decentralized blockchain using smart contracts. Firstly, a permissioned blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Furthermore, based on the smart contract designed each EVs individual needs from CS is satisfied while maximizing its utility

Keywords: Agents and autonomous systems, Computer aided control design
Abstract: In this article, an online collaborative exploration and coverage method is proposed for the unknown complex environment with multiple agents. The exploration and coverage is based on Boustrophedon motion, while the detection conditions for backtracking points have been modified based on mission requirements, the battery charge level of each agent is considered to reduce agent loss, and collision free paths are generated. The proposed method is evaluated in simulation, where complex environment with multiple branches is explored by multiple agents.

Keywords: Control over networks, Large-scale systems, Sampled data control
Abstract: There are several physical systems which exhibit two-time scale property. When these systems are controlled through networks, uniform sampling causes an extra burden on the network bandwidth. Furthermore, uniform sampling of slow and fast variables leads to over-sampling of slow variables. This work proposes an event-trigger theory which leverages the physical property of discrete two-time scale system to reduce the requirement of network bandwidth. Events in slow and fast variables occur as per their respective dynamics and based on the occurrence of a respective event, only slow or fast variables are communicated through a network. Thus the communication of the state is carried out based on an event of the respective set of variables. Simulation studies are presented to validate the theoretical developments.

Keywords: Discrete event systems, Automata
Abstract: Networked discrete-event systems consist of autonomous subsystems that utilise digital communication for solving cooperative control tasks. Each subsystem is composed of a local agent connected to a network unit, which comprise a synchronisation unit and a decision unit. This paper introduces a synchronisation unit that enables the local agents to reach target states autonomously (tracking control) and to execute synchronous state transitions with other subsystems. For designing the decision units, this paper proposes a compositional abstraction method for networked discrete-event systems to obtain a reduced model that describes the cooperative behaviour of the subsystems defined by the synchronous state transitions. If a sequence of synchronous state transitions appears on the subsystem's state trajectory into its target state, then the decision units have to adapt the state trajectory to execute a deadlock-free order of these synchronous state transitions. As the decision units have only local model information, they exchange their reduced abstracted models among each other to locally compose the abstracted overall model. It is proved that each local decision unit can determine the desired execution order of synchronous state transitions as a state sequence in this composed abstracted model.

Keywords: Constrained control, Optimization, Maritime
Abstract: This paper tackles the motion planning problem for a fleet of commercialized USVs (Unmanned Surface Vessel) subject to communication constraints. The main task of the vessels is to provide environmental monitoring, hence the need of reaching multiple a priori given targets which are outside the radio range of the ground station. The solution of the problem is twofold: i) offline flatness-based trajectories are generated for the vehicles while taking into account the connectivity maintenance with the ground station; B-splines parametrization properties ensure the continuous time constraints validation; ii) online a classical LQR controller is implemented while keeping in check the tracking error. The proposed approach is experimentally validated over the USVs platforms of CT2MC.

Keywords: Fault detection and identification, UAV's, Signal processing
Abstract: This paper proposes an actuator fault detection algorithm for vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), based on acceleration signals provided by a high-rate inertial measurement unit. The proposed algorithm is based on time and frequency-domain analysis of acceleration signals and features selection techniques in order to identify the most relevant frequencies of the spectrum and time-domain features involved in the occurrence of the fault. The testing of the algorithm is focused on the damage of a propeller blade and the effects of such damage during vertical take-off and landing, where all the lift is generated by the propellers. The algorithm is tested on real data acquired on the Songbird VTOL UAV [1]. The experimental results prove the possibility of using acceleration signals for diagnostic purposes on VTOL UAVs.

Keywords: Predictive control for nonlinear systems, UAV's
Abstract: This paper focuses on the design and implementation of a two layer hierarchical controller for a tethered aerial vehicle. In particular, a challenging problem of smoothly landing on a inclined plane is considered. Such problem yields nonlinear control objective and constraints which require advanced control techniques like Nonlinear Model Predictive Control (NMPC), that is known to be computationally demanding. A control architecture composed by two layers is proposed: an NMPC operating at 100 Hz is employed to generate feasible trajectories on-line and a simplified local controller, working at 500 Hz, is used to track them. The effectiveness of this hierarchical control structure is demonstrated by a closed-loop simulation. Moreover, the rejection of external disturbances, such as the wind, is a peculiar characteristic of the proposed control strategy.

Keywords: UAV's, Nonlinear system theory, Robotics
Abstract: The control of a quadrotor is typically split into two subproblems: finding desired accelerations to control its position, and controlling its attitude and the total thrust to track these accelerations and to track a yaw angle reference. While the thrust vector, generating accelerations, and the angle of rotation about the thrust vector, determining the yaw angle, can be controlled independently, most attitude control strategies in the literature, relying on representations in terms of quaternions, rotation matrices or Euler angles, result in an unnecessary coupling between the control of the thrust vector and of the angle about this vector. This leads, for instance, to undesired position tracking errors due to yaw tracking errors. In this paper we propose to tackle the attitude control problem using an attitude representation in the Cartesian product of the 2-sphere and the 1-sphere, denoted by S²xS¹. We propose a non-linear tracking control law on S²xS¹ that decouples the control of the thrust vector and of the angle of rotation about the thrust vector, and guarantees almost global asymptotic stability. Simulation results highlight the advantages of the proposed approach over previous approaches.

Keywords: Electrical machine control, UAV's, Filtering
Abstract: The usage of unmanned civil and military drones is steadily rising. Essential components of every multicopter are its electric motors and the electronic speed controllers (ESC). These also determine, besides the flight dynamics and the stability behavior, the flight duration in a crucial way. Therefore in this contribution, an ESC for permanent magnet synchronous motors with a sinusoidal back electromotive force (PMSM) is presented, which was designed, under consideration of all these points. The basis for the precise and dynamic speed control is a high-resolution position sensor in combination with a 72 MHz microcontroller. Under these circumstances, the speed control can operate at a high frequency of 20 kHz together with a high resolution. Using a field oriented control in combination with a flatness based control concept theoretically guarantees a maximum torque per ampere and thus a low energy consumption. The required speed signal is gained from the rotor position using a proportional integral observer. An exact asymptotic regulation is achieved implementing a state feedback integral controller.

Keywords: Linear systems, UAV's
Abstract: In this paper, a decentralized robust tube-based model predictive control algorithm is used for two-dimensional Voronoi-based deployment of a multi-agent system in a bounded convex area, where the planar motion of each agent is subject to uncertain measurements. A bias bounded by a rectangle is thus considered for each agent's position measurement. The convex area of deployment is then partitioned into guaranteed Voronoi cells separated by a bounded unauthorized corridor. By using a decentralized robust predictive control, each agent is guaranteed to evolve inside the safety region defined by the agent's guaranteed Voronoi cell and to converge to a point in a set centered on the Chebyshev center of this cell, driving the multi-agent system into a static configuration. Simulation results show the effectiveness of the proposed decentralized control strategy on a fleet of quadrotors when one of the agents is subject to a measurement bias due to a sensor fault.

Keywords: Maritime, Observers for nonlinear systems, Filtering
Abstract: In underwater navigation the Global Positioning System is unavailable, hence other solutions must be pursued in the development of navigation systems. In this paper, filters for the position, linear velocity, and acceleration of underwater vehicles are derived by combining an inertial navigation system with a long baseline acoustic positioning system. A dynamic model is devised via state augmentation, including bias of the pseudo-ranges, which accounts for the effect of the unknown offset between the emitters' and receivers' clocks. The proposed solution, which includes the explicit dynamic estimation of the bias of the pseudo-ranges, is a linear Kalman filter. The observability of the system is assessed, which allows to establish globally exponentially stable error dynamics. The performance of the solution is evaluated with realistic simulation results, considering sensor noise and discrete-time measurements.

Keywords: Observers for nonlinear systems, Observers for linear systems, Autonomous robots
Abstract: In this paper we consider the complete state estimation problem of a vehicle navigating in a three dimensional space. We assume that the vehicle is equipped with an Inertial Measurement Unit (IMU) and a sensor that provides some information about the position of the vehicle. Example of these sensors are those which provide full position measurements (e.g., GPS), range measurements (e.g., Ultra-Wide Band (UWB) sensors) or inertial bearing measurements (e.g., motion capture cameras). Regardless of the type of sensor used, we propose a generic nonlinear observer that estimates the position, velocity and the orientation of the vehicle along with the gyro bias. Exponential stability of the zero estimation errors is shown with a region of attraction that can be arbitrary enlarged. Simulation results using bearing measurements are provided to illustrate the effectiveness of the proposed estimation approaches.

Keywords: Observers for nonlinear systems, Robotics, Lyapunov methods
Abstract: This paper introduces an advanced Lyapunov stability analysis for an attitude observer that has been developed on the special orthogonal group. In particular, when the attitude observer is constructed based on multiple direction measurements toward known reference points, a local exponential stability has been established by linearization, under the assumption that those reference points are fixed in the inertial frame. Several modifications have been proposed to deal with reference directions changing over time. Here, we present an alternative Lyapunov analysis to show that the attitude observer still exhibits exponential stability for time-varying reference directions, under the assumption that the observer gain is sufficiently large relative to the rate of change of the reference directions. These are illustrated by a numerical example, followed by experimental results with visual marker detection in an indoor space.

Keywords: Observers for nonlinear systems
Abstract: This paper considers aided inertial navigation of unmanned aerial vehicles aided with position measurements from one or more global navigation satellite system antennas, where the exact positions of the antennas are assumed unknown. This reflects that the antennas’ location relative to the inertial sensor, i.e. the lever arms, might be difficult to measure accurately in the coordinate frame of the inertial sensor. Using inaccurate lever arm values will deteriorate both the position and attitude estimates of the vehicle. It is easier to manually and accurately measure the distances from each antenna to the inertial sensor and between antennas, and this information can be used constructively to estimate the lever arms online. In this paper, the distance information is used to reduce the representation of one or more lever arms to two or three states, respectively. An error-state extended Kalman filter is derived and compared to two other similar filters in simulations: one filter in which the lever arms are known and one which represents all lever arms as body-fixed positions. The suggested filter is shown to perform as well as the latter, but with a significantly smaller state space representation.

Keywords: Observers for nonlinear systems, Algebraic/geometric methods, Stability of nonlinear systems
Abstract: This paper presents a nonlinear finite-time stable attitude estimation scheme for a rigid body with unknown dynamics. Attitude is estimated from a minimum of two linearly independent known vectors measured in the body-fixed frame, and the angular velocity vector is assumed to have a constant bias in addition to measurement errors. Estimated attitude evolves directly on the special Euclidean group SO(3), avoiding any ambiguities. The constant bias in angular velocity measurements is also estimated. The estimation scheme is proven to be almost globally finite time stable in the absence of measurement errors using a Lyapunov analysis. For digital implementation, the estimation scheme is discretized as a geometric integrator. Numerical simulations demonstrate the robustness and convergence capabilities of the estimation scheme.

Keywords: Robotics, Autonomous systems, Maritime
Abstract: This paper develops a novel nonlinear control approach for slender-body underactuated underwater vehicles with a body shape symmetric with respect to the longitudinal axis. Compared to aerial vehicles, added-mass effects are much more preponderant and complex for underwater vehicles, especially for slender bodies. By considering an axisymmetric body, these added-mass effects along with dissipative hydrodynamic force are carefully taken into account via various adaptations and decompositions, resulting in a modified ``apparent'' force independent of the vehicle's orientation and subsequently a nonlinear system with a triangular control structure. The proposed controller is then complemented with an integral correction term so as to enhance its robustness with respect to model uncertainties and external disturbances. Comparative simulation results conducted on a realistic model of a quasi-axisymmetric underwater vehicle illustrate the performance and robustness of the proposed control approach.

Keywords: Sliding mode control, Autonomous robots
Abstract: This paper presents a control algorithm for a differential-drive robot following a path. The main contribu- tions are: a new control formulation that does not require the robot global position, and a nonlinear controller based on the sliding mode control approach that guarantees stability in both forward and backward motion. Numerical simulations are provided to validate the proposed algorithm.

Keywords: Sliding mode control, Cooperative autonomous systems, Robust control
Abstract: This paper discusses the effects of sliding mode based platooning algorithms in presence of unmodeled dynamics. The inevitable periodic motions of the sliding variables are investigated and their impact on a platoon are discussed. Conditions on the parameters of a control law are formulated such that string stability is achieved. A design technique based on describing functions of a first order sliding mode controller and the suboptimal controller is proposed. Simulation and experimental results on small-scale vehicles support the presented results.

Keywords: Sliding mode control, Feedback linearization, Aerospace
Abstract: For mitigating the chattering effect in the sliding mode control (SMC), many adaption mechanisms have been proposed to reduce the switching gains. However, less attention is paid to the control structure, which influences the resulting uncertainty term and determines the minimum possible gains. This paper compares three control structures for inducing higher-order sliding modes in finite time: nonlinear dynamic inversion (NDI) based SMC, higher-order sliding mode control (HOSMC) with artificially increased relative degree, and the recently proposed incremental nonlinear dynamic inversion (INDI) based SMC. The latter two control structures have reduced model dependency as compared to NDI-SMC. Moreover, their nominal control increments are found to be approximately equivalent if the sampling interval is sufficiently small and if their gains satisfy certain conditions. Under the same circumstances, the norm value of the resulting uncertainty using INDI-SMC is several orders of magnitude smaller than those using other control structures. For maintaining the sliding modes, the minimum possible gains required by HOSMC approximately equal those needed by INDI-SMC divided by the sampling interval. Nevertheless, these two approaches have comparable chattering degrees, which are effectively reduced as compared to NDI-SMC. The analytical results are verified by numerical simulations.

Keywords: Sliding mode control, Robotics, Autonomous robots
Abstract: Reaching the home position especially when it is not described using coordinates, is an important task in many autonomous robotic applications. This work investigates the aforementioned autonomous homing problem with the design goal of applying in small size robot, which has sensing and processing constraints, along with robust controller design. The novelty in the work attributes to the proposed sliding surfaces, one for yaw-control and the other one for pitch-control, whose intersection aligns the robot's orientation towards the home position irrespective of the robot's location. Finite time convergence to the sliding surface with partial state-feedback is proved with the help of Lyapunov analysis and further, the reduced order dynamics is analyzed to show the convergence of the robot within a threshold distance to the home position. The proposed control law is verified using simulations in Matlab in the presence of matched disturbances.

Keywords: Sliding mode control
Abstract: Novel robust finite time stabilizing algorithms are proposed in [1] for a nonlinear first and second order systems under sufficiently large or linear growing input disturbance. In the present paper the proposed algorithms are generalized under simultaneous presence of large input and output disturbances. This problem is not trivial and complicated, because simultaneous compensation of input and output disturbances leads to the failure of objective or the loss of closed-loop system stability. Thus, in the paper we synthesis algorithms and define the class of disturbances such that the closed-loop system preserves the stability and goal is fulfilled. The control scheme is based on the disturbance compensation, relying on the dirty differentiation, and sliding mode approaches. The sufficient conditions of the closed-loop system stability under admissible disturbances are given. Simulation results illustrate efficiency of the proposed schemes and support theoretical results.

Keywords: Switched systems, Markov processes, Optimal control
Abstract: In most real cases transition probabilities between operational modes of Markov jump linear systems cannot be computed exactly and are time-varying. We take into account this aspect by considering Markov jump linear systems where the underlying Markov chain is polytopic and time-inhomogeneous, i.e. its transition probability matrix is varying over time, with variations that are arbitrary within a polytopic set of stochastic matrices. We address and solve for this class of systems the infinite-horizon optimal control problem. In particular, we show that the optimal controller can be obtained from a set of coupled algebraic Riccati equations, and that for mean square stabilizable systems the optimal finite-horizon cost corresponding to the solution to a parsimonious set of coupled difference Riccati equations converges exponentially fast to the optimal infinite-horizon cost related to the set of coupled algebraic Riccati equations. All the presented concepts are illustrated on a numerical example showing the efficiency of the provided solution.

Keywords: Linear systems, Electrical machine control, Optimization
Abstract: This paper investigates fractional- and distributed-order proportional-integral-derivative (FOPID and DOPID) controllers for permanent magnet synchronous motors. The design of FOPID controllers requires the setting of four parameters, i.e. the gains of the three different actions and the non-integer order of differentiation, because the non-integer order of integration is fixed to an optimal value. An efficient particle swarm optimization (PSO) sets the parameters in two steps: firstly for an inner loop, secondly for an outer loop. The DOPID controller is characterized by seven different orders of integration or differentiation. In this second case, the tuning is also performed by PSO in two steps. In both cases, the feedback transfer function of the inner loop is a non-integer order plant for the outer loop, then the outer controller should be of fractional order to obtain the best performance/robustness indexes, as shown by simulation.

Keywords: Biomedical systems, Modeling
Abstract: In this paper, a centralized model predictive control algorithm to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is proposed. The algorithm is tested in simulation on a hypnosis-hemodynamic combined model for use during general anesthesia. The preliminary results are promising and show the effectiveness of the control procedure. The anesthesiologist will allows be in the loop and act as the primary controller by making the Remifentanil blood concentration setpoint changes based on observation about anesthetic depth.

Keywords: Identification, Modeling, Robotics
Abstract: Ionic polymer metal composite (IPMC) actuators have promising applications in robotics and microrobotics in a not distant future, but this will require a deep knowledge in different aspects, such as manufacturing, material characterization and control strategies. In this paper, frequency responses of several IPMC actuators, cut from the same IPMC bulk sheet with a micro laser etching machine, are obtained experimentally by measuring the tip deflection by means of a laser distance meter. Models, consisting of a fractional integrator in series with a resonant system of both fractional and integer order, are identified in the frequency range [0.11,30] Hz for each sample of IPMC. The results show that models with these structures can be adequate for control purposes.

Keywords: Emerging control theory, Mechatronics, Biological systems
Abstract: In this paper, Fractional Order Elements FOE, fabricated by using a carbon black nano structured dielectrics, is presented and utilized as capacitor in a fractional order RC circuit. Results on the experimental frequency characterization of one FOE device are given as well as the responses of passive and active fractional RC circuits. The analytical response of the fractional RC circuit has been used to identify the value of the pseudo capacitance characterizing the considered FOE.

Keywords: H2/H-infinity methods, Uncertain systems, Modeling
Abstract: This paper presents an approach to design Linear Quadratic control laws for commensurate fractional order models. The proposed approach is based on the reformulation of a commensurate fractional order model as an uncertain integer order model. A Linear Quadratic control law is then designed from the uncertain integer order model as it can be expressed as a multiobjective H2/Hinf problem which is solved using Linear Matrix Inequalities based approach. The entire approach is applied to an academic example.

Keywords: Linear parameter-varying systems, Identification
Abstract: In the paper a block--matrix form of the variable--, fractional--order linear time--invariant difference equation is investigated. It is proved that similar to the state--space representation but with linearly increasing matrix--vector dimensions is useful in the difference equation solution. The block diagram of the system described by the Jordan--like canonical form. It uses the variable--, fractional--order integrators and proportional blocks. The proposed approach simplifies numerical calculations of solutions due to a possibility of parallel computations. Analysis is supported by the numerical example.

Keywords: Automotive, Autonomous systems, Linear parameter-varying systems
Abstract: The paper proposes a robust Linear Parameter-Varying (LPV) control, in which the results of the approximation of neural network is built. The motivation of the paper is to replace the neural network feedback control with an LPV control architecture, in which some signal values are set by the neural network. Thus, the stability of the closed-loop system through the robust LPV design is guaranteed, while the performances of the neural network are preserved. In the method the output signal of the neural network is approximated by a formulation, which incorporates the scheduling variable and an additional input disturbance in the output of the LPV control. The selections of the scheduling variable and disturbance values lead to an iterative design of the LPV control. The proposed method is applied to the design problem of the autonomous steering control, whose efficiency is presented through simulation examples.

Keywords: Automotive, Energy systems, Optimal control
Abstract: Electrification of powertrains is closely related to the design of intelligent control strategies that are applied to improve the operation of vehicle propulsion units such that reduced fuel consumption and optimized usage of the energy storage system is achieved. Thereby, the design of energy management strategies for real-world driving cycles, i.e., for conditions where only limited and uncertain information about the future driver torque demand is available, represents an emerging field of research. In this contribution, we evaluate the performance of deterministic and uncertainty-aware model predictive controllers for fuel economy optimization of hybrid electric vehicles on (uncertain) real-world driving cycles, i.e., when the driver action significantly deviates from the expected one. In a performance study, using dSPACE Automotive Simulation Models, these controllers are compared in terms of fuel economy, charge sustainability and computational complexity.

Keywords: Automotive, Optimization
Abstract: The various operating scenarios of hybrid electric vehicles (HEVs) require the design of suitable power splitting strategies for the hybrid drivetrain. A compromise between low complexity and high efficiency is always an issue for the power management implementation. In this paper the parallel HEV is assumed to operate under a power splitting strategy based on vehicle velocity and battery state of charge thresholds. The off-line minimization of a cost function related to the vehicle electrical and fuel consumptions determines the velocity-based quantization levels and the corresponding ratios of the power splitting between the actuators. The effectiveness of the proposed approach is verified through numerical simulations of the new European driving cycle and the worldwide harmonized light vehicle test procedure.

Keywords: Automotive, Machine learning, Predictive control for linear systems
Abstract: In the last few decades vehicles have been equipped with an increasing number of sensors. These sensors provide a large amount of data about the vehicle and its environment. This data might also contain hidden information about the vehicle, which can be revealed using big data approaches. The paper presents the design of an MPC-based (Model Predictive Control) lateral controller for autonomous vehicles in which the results of the big data analysis are exploited. The data are provided by the high-fidelity car simulation software CarSim. Moreover, in the big data Analyses, the C4.5 decision algorithm is used, which is augmented with the MetaCost algorithm. The results of the analyses are used as constraints in the MPC design. Finally, the efficiency of the control system is examined through a complex simulation example.

Keywords: Automotive, Predictive control for nonlinear systems, Optimal control
Abstract: This paper implements an ecological cooperative adaptive cruise control (Eco-CACC) on the ego battery electric vehicle (BEV) of a two-vehicle platoon by using a Nonlinear Model Predictive Control (NMPC) framework. By considering various speed profiles of the reference vehicle, this approach balances an inter-vehicular distance reduction with a velocity profile smoothing in order to minimize powertrain energy consumption. Although a short inter-vehicular distance benefits from the slipstream effect, the power losses due to an aggressive tracking can nullify such a benefit. Considering a spontaneous platoon with this approach, in a realistic driving case scenario a BEV can reduce its energy consumption by up to 40% as com- pared to a conventional adaptive cruise control (ACC) strategy. Thus, a proof-of-concept for the NMPC implementation of an Eco-CACC approach is provided. Furthermore, the reductions in savings due to horizon shortening are assessed by Dynamic Programming (DP).

Keywords: Modeling, Identification for control, Optimization
Abstract: Typically, in the automotive field, classic estimation or filtering techniques (e.g. Least Squares, Kalman Filter) are used to characterize models. However, in scenarios in which there is a lack of data or some quantities are not available from CAN (Controller Area Network) acquisitions or from datasheets, only simple and poorly accurate models can be obtained. This paper presents an innovative approach that allows avoiding these difficulties: the Genetic Algorithm applied to parameters estimation of powertrain models for the longitudinal dynamic of the vehicle. By employing this technique, parameters estimation is possible even for particularly complex models. The considered approach was tuned and tested in simulation environment showing promising results. Thereafter, the CAN acquired target quantities needed for the minimization, e.g. the engine torque and the wheel speed, and the outputs of the Genetic Algorithm identified model have been compared showing the effectiveness of the proposed approach in real data validation.