Keywords: Automotive control, Autonomous systems, Image processing
Abstract: This paper presents a monocular camera-based lead vehicle distance and velocity estimation algorithm for automotive application. With an initial guess of real width of the lead vehicle, a Kalman Filter gives estimates for relative distance, velocity and acceleration. The still unknown scale factor to the real size is then statistically estimated from multiple hypothesis using vertical triangulation measurements. Camera pitching motion effects are compensated through the estimation of the vanishing point. The real relative distance, velocity and acceleration can be obtained with the estimated scale factor. The developed method is evaluated in simulations considering the Euro NCAP forward collision warning and emergency braking test procedures, the braking dynamics of vehicles, multiple lead vehicle sizes, periodic camera pitching disturbance, pixelization and vanishing point estimation errors and a wide range of velocities from 10km/h to 130km/h. The results are promising and so real life evaluation is the goal of future development.

Keywords: Autonomous systems, Intelligent transportation systems
Abstract: The automotive industry is heading towards the introduction of fully autonomous vehicles. However before these type of vehicles are commercially available at mass scale, some issues need to be solved. A major issue is the ethics involved in the decision-making during an accident; this paper presents an analysis of how to solve this issue. The proposal is a preprogrammed system with different ethical settings based on six formal ethical theories. For the implementation, eight ethical concerns are defined and ordered according to each theory. These concerns are defined in terms of harm to self and harm to others. The ethical concerns are used as a guideline to define the level of importance of each person or object in an accident scenario. With the proposed system, the vehicle will be partially tailored to the preferences of different users while still being bounded by legal requirements to avoid any misuse.

Keywords: Autonomous systems, Marine control, Optimisation
Abstract: This paper presents an Autonomous Underwater Vehicle (AUV) with a dynamic configuration of its actuation. The AUV is able to modify its configuration online (at each sampling time) during its missions. A procedure to optimize the dynamic configuration with respect to energy-like criteria is introduced. The simulation results are shown to prove the efficiency of a dynamic management of the actuation configuration.

Keywords: Autonomous systems, Robotics, Nonlinear systems
Abstract: This paper deals with the trajectory tracking control problem in the kinematic bicycle model. To solve the control problem when there are several inherent constraints in the system we apply a sigmoid function (hyperbolic tangent) in the feedback loop. We present an error function between the real and virtual vehicles (when the latter moves along the required trajectory) and introduce a control law that ensures that the zero error is an asymptotic equilibrium point. A number of examples demonstrate the characteristics of the control law and its performance.

Keywords: Education and training, Robotics, Intelligent transportation systems
Abstract: In this paper, we describe a laboratory setup made with three wheeled mobile robots, the Husarion ROSbots, for implementing and testing autonomous driving maneuvers. First, we implement a lane detection algorithm and an adaptive cruise control to enable the ROSbots to follow lanes and adapt their longitudinal speed. Then, we set up a wireless communication scheme to enable the ROSbots to perform safe overtaking maneuvers. Finally, we demonstrate the developed algorithms via laboratory experiments in the DCSC Lab at TU Delft.

Keywords: Robotics
Abstract: The usage of Micro Aerial Vehicles (MAVs) in different applications is gaining attention, however one of the main challenges is to provide collision-free paths, despite the uncertainties in localization, mapping, or path planning. This article proposes a novel collision free path planner for MAVs navigation in confined environments, while not being dependent on the information of the localization and only relying on 2D local point-cloud data. The proposed backup path planner generates velocity commands for a trajectory follower controller, while guaranteeing a safety distance from all points in the local-point-cloud. The proposed method considers the kinematics of the MAV and can be extended to any robotics application, such as ground vehicles. The proposed method is evaluated in a Gazebo simulation environment and successfully provides a collision-free navigation.

Keywords: Automotive control, Signal processing, Fault diagnosis
Abstract: In this paper is presented a method for open phase fault detection with applicability in electric drive trains. The strategy is simple and requires only a limited number of hardware components available on most common hardware platforms for power electronics. The method is based on the computation of cross-correlation between each phase current and is simple and robust enough to be used in real-time automotive applications. The algorithm is tested with recorded data acquired from a Permanent Magnet Synchronous Machines (PMSM) motor-load test bench used for high power Hybrid Electrical Vehicles (HEV). The experimental results sustain the applicability of the algorithm to HEVs open phase fault detection.

Keywords: Fault diagnosis, Aerospace control, Prognostics and diagnostics
Abstract: Abstract— In this paper PCA and D-PCA techniques are applied for the design of a Data Driven diagnostic Fault Isolation (FI) and Fault Estimation (FE) scheme for 18 primary sensors of a semi-autonomous aircraft. Specifically,Contributions-based, and Reconstruction-based Contributions approaches have been considered. To improve FI performance an inference mechanism derived from evidence-based decision making theory has been proposed. A detailed FI and FE study is presented for the True Airspeed sensor based on experimental data. Evidence Based Filtering (EBF) showed to be very effective particularly in reducing false alarms.

Keywords: Fault diagnosis, Modelling and simulation
Abstract: Sensor data can be used to diagnose the system's health. A challenge comes when the data contain missing or invalid data. It is common that sensors misread for various reasons. So, data contain missing measurements and sensor saturation. The main contribution in this paper is to implement a method based on the Input-Output Hidden Markov Model that trains the model using the missing measurements and sensor saturation, then diagnoses the system health at given operating conditions. Usually, if a data set contains some sequences with missing elements then they can be excluded from the analysis. It cleans the data set but reduces its size. This strategy knows as list-wise or case-wise deletion is less suitable for real application cases. The proposed method includes the sequences with missing data into the analysis by generating the missing elements to complete the sequence. The maximum likelihood is applied to estimate IOHMM parameters that offer substantial improvements over list-wise deletion. A numerical application with simulated data sets illustrates the method.

Keywords: Fault diagnosis, Multi-agent systems, Linear systems
Abstract: This paper deals with the problem of distributed fault detection for a team of multi-agent systems with linear dynamics using output observers. The proposed output observer is employed to estimate the state of virtual models based on relative outputs in order to generate a set of residual signal that are indicative of the presence of a fault. The convergence of the observer is proven for any initial condition and fault detectability conditions are defined. The proposed method ensures distributed actuator fault detection using input-output relations, where each agent is capable of detecting not only its own faults, but also those that occur in its neighbours solely by using exchanged relative outputs. Results of numerical simulations are provided to show the robustness of the proposed approach.

Keywords: Fault diagnosis, Optimisation, Switching systems
Abstract: This paper deals with fault detection (FD) for a class of discrete-time switched systems with sensor faults. Under the assumption that disturbances and measurement noise are unknown but bounded, two methods, respectively based on the H_infty and the L_infty formalisms, are introduced to attenuate the effects of the uncertainties and to improve the accuracy of the proposed residual framers. The design conditions of the interval observer are given in terms of Linear Matrix Inequalities (LMIs). Furthermore, a FD decision is developed to indicate the presence of faults. A numerical example is performed to illustrate the effectiveness of the proposed method based on the L_{infty} performance through a comparison with the results obtained using the H_{infty} analysis.

Keywords: Prognostics and diagnostics, Modelling and simulation, Complex systems
Abstract: This paper proposes a novel approach for Remaining Useful Life (RUL) forecasting using interval model-based prognostics techniques based on zonotopes without prior knowledge of the degradation behaviors of the system. Although Kalman filtering techniques have proved their estimation ability with Gaussian noises, an interval approach with zonotopic sets technique has been integrated for optimal estimation of parameters with unknown-but-bounded noises. Moreover, the proposed model-based prognostics technique has been applied to a DC-DC converter described as a nonlinear dynamical system affected by degradation behaviors. Thus, the estimated degraded parameters are adopted in the RUL prediction technique that propagates the zonotopic sets until the End-of-Life (EoL) of the system. In general, the technique is split into estimation and prediction phases using Zonotopic Extended Kalman Filter (ZEKF) to deal with the nonlinearities of the system and compute the optimal observer gain. A DC-DC converter case study in simulation is used to illustrate the utilized techniques and the simulation results prove the effectiveness.

Keywords: Optimisation, Computational methods, Multi-agent systems
Abstract: We present semi-decentralized and distributed algorithms, designed via a preconditioned forward-backward operator splitting, for solving large-scale, decomposable semidefinite programs (SDPs). We exploit a chordal aggregate sparsity pattern assumption on the original SDP to obtain a set of mutually coupled SDPs defined on positive semidefinite (PSD) cones of reduced dimensions. We show that the proposed algorithms converge to a solution of the original SDP via iterations of reasonable computational cost, numerically comparing their performances with respect to others available in the literature.

Keywords: Multi-agent systems, Robotics, Industrial automation, manufacturing
Abstract: With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters.

Keywords: Multi-agent systems, Swarms, Networked systems
Abstract: This paper addresses the problem of having a multi-agent system converging to a rendezvous location for networks of agents without any type of localization sensor. A central node or tower is able to determine the noisy position of each agent and transmit it using a directional antenna. Given the asynchronous communication setup, the topology will not be connected in general, which precludes the use of set-consensus algorithms available in the literature. By devising a flocking rule with mechanisms to prevent collisions, nodes explore the mission plane while maintaining the current connectivity. The process is followed by a convergence phase using a modified set-consensus algorithm with collision-free guarantees and asymptotic convergence. Results are also presented that, if there is a sufficient density of nodes, convergence occurs to a single cluster. The performance of the proposed algorithm is assessed through simulations, illustrating the cases where convergence occurs to a single or multiple clusters.

Keywords: Nonlinear control, Multi-agent systems
Abstract: The paper proposes an estimation and control method about sustaining the motion of a group of autonomous agents under the Generalized Cyclic Pursuit (GCP) laws, where formation patterns can be formed by assigning eigenvalues of the system to be marginally stable. In the present paper, a Linear Quadratic Estimator (LQE), used to estimate the absolute position based on information exchange, is coupled with a Model Reference Adaptive Control (MRAC) to sustain the motion of agents and thus maintain the desired patterns in the presence of uncertainties and noise. Simulation results are provided to verify the proposed approach in area coverage applications.

Keywords: Nonlinear systems, Complex systems, Multi-agent systems
Abstract: In this paper, the problem of estimation of the unknown parameters in the complex networks composed of identical nodes being generalized Lorenz chaotic systems will be discussed. The estimation is provided by an adaptive observer. Estimation of the parameters is provided on the complex network with ring topology and bidirectional coupling between nodes. The extra node being an adaptive observer is connected with the permanent node of the analyzed complex network. During the relatively short period, this additional node will be identically synchronized with the analyzed complex network without precise knowledge of the unknown parameters in the permanent nodes. The results are illustrated by the numerical simulation as well.

Keywords: Decentralized control, Multi-agent systems, Optimisation
Abstract: This paper addresses the problem of controlling a large set of agents, each with a quadratic utility function depending on individual combinatorial choices, and all sharing an affine constraint on available resources. Such a problem is formulated as an integer mono-constrained bounded quadratic knapsack problem. Differently from the centralized approaches typically proposed in the related literature, we present a new decentralized algorithm to solve the problem approximately in polynomial time by decomposing it into a finite series of sub-problems. We assume a minimal communication structure through the presence of a central coordinator that ensures the information exchange between agents. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are discussed, highlighting that the algorithm constitutes a fully polynomial approximation scheme. Numerical experiments are presented to show the effectiveness of the approach in the optimal resolution of large-scale instances.

Keywords: Feedback stabilization, Robust control, Time-delay systems
Abstract: This paper deals with the stabilization problem of constrained uncertain fractional order positive systems with time delay. Non symmetrical constraints with polytopic and interval uncertainties are considered. The designed controller is computed using linear programming technique. A numerical example is given to illustrate the usefulness of the proposed results.

Keywords: Robotics, Time-delay systems, Disturbance rejection
Abstract: The nonlinear dynamic description of a wheeled platform with active and passive resonators carrying a single link manipulator on an uphill/downhill road path including the road disturbance forces applied to the wheels is presented. The linear approximant of the description is derived to be in a singular neutral time delay system form. The design goal is to control the distance of the load from the road and the speed of the platform despite the presence of the disturbances. The approach is that of exact model matching with simultaneous disturbance rejection for singular neutral time delay systems. The matrices of the general controller are derived. The performance of the proposed control scheme is demonstrated through computational experiments upon the nonlinear description and the linear controller.

Keywords: Robust control, Time-delay systems, Nonlinear systems
Abstract: This work deals with the stability analysis of linear discrete-time time-variant systems, focusing on the most restrictive stability measures for free respectively forced systems, the exponential stability respectively BIBS (bounded-input bounded-state) stability. Additionally the connection of these stability concepts with the so-called Bohl exponent is in focus of this work. The Bohl exponent can be seen as a generalization of eigenvalues for the stability analysis of time-variant systems. The contribution of this work is the combination of the exponential and BIBS stability analysis with the numerical computation of the Bohl exponent, leading to a numerical stability analysis for linear discrete-time time-variant systems. This stability analysis is applied at a time-variant drivetrain control loop.

Keywords: Switching systems, Time-delay systems, Linear systems
Abstract: This paper devotes to propose control design methods imposing general quadratic and bounded peak to peak gain performances on discrete-time linear switched positive systems with time-varying delay. General quadratic constraints are limitations that are modeled as a quadratic matrix to make a relationship between exogenous input and output signals. Moreover, the problem of bounding outputs peak amplitude for bounded disturbance inputs is named bounded peak to peak gain performance. Sufficient conditions are derived for the existence of a set of state feedback controllers guaranteeing the closed-loop switched system with time-varying delay in states not only is positive and globally uniform exponential stable but also has two stated performances for switching signals with an average dwell-time, which is greater than a positive certain constant. By using the Lyapunov-Krasovskii functional theorem, these conditions are formulated in terms of linear matrix inequalities. The quadruple tank system model is employed to illustrate the effectiveness of the proposed method.

Keywords: Time-delay systems, Disturbance rejection, Automotive control
Abstract: The problem of diagonal Decoupling with simultaneous Disturbance Rejection (DDR) is studied for the class of general neutral multi delay systems with measurable and nonmeasurable disturbances via delayless dynamic and/or static controllers. The delayless controllers use the measurement outputs and the measurable disturbances. The necessary and sufficient conditions for the problem to have a solution are established and the general form of the delayless controller matrices solving the problem are derived. The results are successfully applied to a motion control problem.

Keywords: Fuzzy logic and fuzzy control, Time-delay systems, Optimisation
Abstract: This paper is focused on non-quadratic SOF control for delayed Takagi Sugeno (T-S) models with input saturation and external disturbances. A polytopic representation is first used to describe the input nonlinearity. By using a descriptor redundancy approach, an augmented form of the closed-loop system is established. Then, in order to reduce the conservatism of the quadratic approach, a poly-quadratic function and a Lyapunov-Krasovskii function (LKF) are used to derive H∞ stabilization conditions. The design conditions are formulated and resolved in LMIs terms. A simulation example is made to show the successful of the proposed method.

Keywords: Decentralized control, Predictive control, Cyber-physical systems
Abstract: This article describes the design of distributed Model Predictive Control (MPC) strategies based on a linear model of a water delivery canal, composed of three pools, each ending with a gate. The distributed MPC control algorithm relies on a Distributed Alternating Direction Method of Multipliers (D-ADMM) optimization framework. A non-linear model of the canal is used to identify a linearized model around an equilibrium point, which is then used to design the controllers. Since the plant states are not accessible, a Kalman filter is used to estimate them. Integral action is also included together with an anti-windup algorithm. Simulations using the nonlinear plant model are presented to show the impact of control algorithm parameters on performance.

Keywords: Game theory, Predictive control, Robust control
Abstract: This paper develops a robust path integral (RPI) model predictive control using the Monte Carlo (MC) sampling to address the optimal control (OC) problem for the stochastic differential game (SDG). The two-player zero-sum differential game has been extensively investigated, mostly as its outcome indicates the H_{infty} optimality. The proposed path integral (PI) control framework provides an analytically sound method for building an algorithm of optimal control for this game based on stochastic trajectory sampling. This is achieved by using Feynman-Kac (F-K) lemma which transforms the value function of stochastic optimal control (SOC) problem into an expectation over all probable trajectories. This transformation makes it possible to solve SOC problems through MC sampling of stochastic processes. Finally, the RPI model predictive control using MC sampling is efficiently implemented for an inverted pendulum system. The RPI control has achieved good performance for changes in inverted pendulum weight and friction when the complete nonlinear swing-up is concerned while such environmental adjustments are not dealt with in a regular PI control.

Keywords: Linear systems, Predictive control, Process control
Abstract: This paper presents discussions on offset-free model predictive control (MPC) methods for linear discrete-time systems in the presence of deterministic system disturbances. The general approach is based on the use of a disturbance model and an observer to estimate the disturbance states. The recent development in offset-free MPC has established the equivalence of the velocity form (without output delay) to a specific choice of the disturbance model and observer. In this note, it was shown that this particular disturbance model and observer is not necessarily equivalent to the velocity form with output delay. Nevertheless, it was shown that the velocity form with output delay is equivalent to a different choice of the disturbance model and observer. An import of this result is that the velocity forms (with and without delayed output) belong to the same general approach - disturbance model and observer. Furthermore, areas that may be considered in future researches are also highlighted. Index Terms—Model predictive control, disturbance rejection, linear discrete-time systems, offset-free control.

Keywords: Predictive control, Automotive control, Nonlinear control
Abstract: Innovative air path concepts utilizing exhaust gas recirculation (EGR) for turbocharged gasoline engines show promising results to lower the CO2 and pollutant emissions. However, the increased complexity of such systems poses high demands on the process control. With the upcoming real-driving emission (RDE) legislation, accurately managing transient operation will be one of the major challenges for the application of these systems, as rapid changes in engine speed have a considerable influence on the boost pressure buildup as well as on the obtained EGR rate.

This paper focuses on the simultaneous control of boost pressure and EGR rate for a two-stage turbocharged gasoline engine with low pressure EGR during highly transient engine operation. When accelerating a vehicle in low gears, the engine speed gradient is at its maximum. This causes the highest difficulty in adjusting the respective setpoints. To tackle this challenge, a data-based nonlinear model predictive controller is presented which explicitly considers the actual engine speed and its predicted future trajectory. After implementation of the new control system into the ECU of a prototype test vehicle, the validation of the control algorithms is done by conducting real-driving experiments on a test track.

Keywords: Predictive control, Distributed systems, Process control
Abstract: Several distributed model predictive control (MPC) strategies have been presented in the last few years. Those strategies are usually suited for large applications, when it is not interesting to use centralized controllers. Sub-optimal solutions are normally achieved by those controllers, although most of them can converge to optimal solutions as well. This paper presents a distributed MPC strategy with guaranteed feasibility and stability. Infinity horizon and cooperation between controllers are considered this new strategy.

Keywords: Predictive control, Optimisation, Process control
Abstract: In this paper, a steady state real time optimization (SRTO) using transient measurement, implemented with a zone control model predictive control (MPC) is applied to a gas-lift system. The use of zone control MPC is suitable for processes where the exact values of the controlled output are not required, but they must be within a specified desirable zone. The control strategy allows the controller to focus on reaching the desired input target supplied by the optimization layer, as long as the outputs are kept within their specified zones and dynamic constraints are respected. With the focus on practicality of application, available, established and successfully implemented process optimization and control techniques are harnessed; through some modifications to fit the process and achieve an optimum. Simulations show that the controller can drive the process to and keep the output within their specified region and also track the desired input target, even in the presence of disturbance.

Keywords: Autonomous systems, Embedded control systems, Fault tolerant control
Abstract: This paper presents an extensive discussion between two control allocation methods for hexacopters. The first one is the so-called classical control allocation (CCA) based on a pseudo-inverse matrix, and the second one is the weighted control allocation (WCA) based on a weighted pseudo-inverse matrix. Control allocation is about mapping the virtual control vector v = [T; L;M;N]^T made of thrust, roll, pitch and yaw torque, respectively, to the individual motors’ speed. The CCA method is studied under motor saturation circumstances, in which CCA can lead to a crash. The WCA method on the other hand displays better performance than CCA for: a) motor saturation, b) vehicle’s mass changes, 3) flight-controller poorly tuned parameters, and 4) motor-fault tolerance. The performance comparison between these two control allocation techniques is made through simulations and real-flight tests.

Keywords: Hybrid systems, Aerospace control, Autonomous systems
Abstract: This work presents a fused-PID (FPID) control strategy for a tilt-quadrotor VTOL (vertical takeoff and landing) where the FPID generates attitude setpoints based on the pilot's velocity commands. The FPID controller consists of two separate PID controllers where one can handle fixed-wing aircraft and the other is designed for multicopters. The output of both controllers is fused depending on the airspeed to obtain an attitude setpoint and a tilt angle of the rotors. In contrast to the commonly-used binary switch between helicopter flight (propellers pointing upwards) and airplane flight (propellers pointing forwards), the FPID makes a smooth transition between the two flight modes possible. The proposed controller design has been extensively tested in simulation and with a real radio-controlled tilt-quadrotor VTOL aircraft. The results presented in this work clearly show that the FPID approach, despite its simplicity, is suited very well for tilt-rotor VTOL aircraft.

Keywords: Hybrid systems, Unmanned systems, Real-time control
Abstract: This work presents a novel control allocation strategy for model tilt-rotor type VTOL aircraft. The aircraft considered has four tilting propellers and the aerodynamic actuators of an airplane. Control allocation converts the high-level commands in terms of total desired thrust and torques into commands distributed among the vehicle’s eleven actuators. The proposed method independently tilts two pairs of propellers to generate torque along the thrust vector, which results in close-to-optimal solutions while being computationally efficient. This approach showed promising results both in simulation and tests on the vehicle and can be used in combination with any controller that generates a two-dimensional thrust and a three-dimensional torque vector.

Keywords: Modelling and simulation, Nonlinear control
Abstract: This paper presents a Model Predictive Control (MPC) based control structure for a convertible Unmanned Aerial Vehicle (UAV) with fixed wings and tiltrotor thrust vectoring. The controller encompasses full flight envelope trajectory tracking, thereby optimally exploiting the aircraft’s Vertical Take Off and Landing (VTOL) and cruising-forward flight capabilities. An adaptive control allocation is designed to handle the changing control authorities of the actuators and efficiently distribute the required control actions between propellers, tilt servos and control surfaces. Preliminary simulation results show the feasibility of the proposed control approach.

Keywords: Complex systems, Modelling and simulation, Robotics
Abstract: In this paper, we propose a new design of unconventional quadrotors with simple mechanisms. They are able to change the orientation and the length of their arms independently thanks to their adaptive morphologies, these quadrotors can be used for specific missions and in more congested spaces. Thus, the usefulness of certain configurations that our quadrotors can make will be shown. In addition, a detailed and generic model is proposed for three possible cases: 1) the extension of the arms, 2) the rotation of the arms, 3) the hybridization between the extension and rotation of the arms. We stress that the modeling of these unconventional quadrotors is more challenging compared to the standard quadrotors, because of the geometrical asymmetry of the quadrotor structures and the constant change of the inertia and the center of gravity during the flight. In this work, a preliminary prototype of our quadrotor with rotating arms is presented with the electronic architecture.

Keywords: Optimisation, Nonlinear systems, Switching systems
Abstract: When applying visual servo algorithms for unmanned aerial vehicles (UAVs) using visual registration targets, it is often desirable that the UAV would retrieve targets after the mission. In this study we plan an energy-optimal trajectory for a UAV to retrieve visual registration targets dropped on the initial flight and then construct a controller to track this trajectory in a stable way. The UAV kinematics are described as a Hamiltonian system on the Lie group SE(3); the boundary value problem (BVP) arising from the geometric framing of Pontryagin’s Maximum Principle (PMP) is then applied. This BVP is solved using a numerical shooting method with a novel multi-arc interpolation approach determining simple optimal arcs between the targets using a semi-analytical Lie group integrator. A switching controller compensating for the switch between in-ground and out-of-ground dynamics is combined with a disturbance observer to create a controller to track the trajectory in a stable way under the ground effect and bounded disturbances. Simulation results complete the work.

Keywords: Automotive control, Fault tolerant control, Renewable energy and sustainability
Abstract: The electrical propulsion system in Hybrid Electrical Vehicle (HEV) contains: the electric machine, the controller and the inverter. Several paper model and interpret each component separately without considering the interaction between those elements. In this paper, the electrical aspect of the inverter will be investigated as a preface to build a global model combining the inverter and the electrical machine. Hence the electrical model of the inverter will be built. Then, electrical faults in the inverter will be integrated and the impact of this faults on the electrical and electromagnetic performance of the electrical machine will be displayed.

Keywords: Power systems and smart grid, Renewable energy and sustainability
Abstract: This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers’ requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system.

Keywords: Power systems and smart grid, Renewable energy and sustainability, Cyber-physical systems
Abstract: This paper presents an optimisation problem to determine the optimal reclosure order of remotely operable switches deployed in a smart grid consisting in a distribution network equipped with one or more Energy Storage Systems (ESS). The proposed solution integrates nonlinear real and reactive power flow equations, by reconducting them to a set of conic constraints, together with several network operator requirements, such as network radiality and ampacity limits. A numerical simulation validates the approach and concludes the work.

Keywords: Renewable energy and sustainability, Bond graph, Modelling and simulation
Abstract: This work is an attempt to develop and validate a graphical dynamical model of an AEM electrolysis cell based on Bond Graphs, an energy based tool that allows to represent multiphysics systems. The model of the cell lays a foundation for developing a complete representation for AEM electrolysers which can be used for simulation as well as for developing control algorithms and fault diagnosis. Parameter identification and model validation is achieved using experimental data.

Keywords: Renewable energy and sustainability, Bond graph, Modelling and simulation
Abstract: This paper proposes new tools to design models and controllers for power systems. Within the port-Hamiltonian modeling framework, concepts that extend passivity margins and energy tanks are used to estimate power reserves in Hybrid Renewable Energy Systems. The methodology is applied to multi-source cells with renewable energy dedicated to the production of clean hydrogen. This work can be extended to more complex networked power systems, with the aim to increase the reliability of standalone networks by fitting the power demands while avoiding damage and extending lifetime of the equipment.

Keywords: Renewable energy and sustainability, Prognostics and diagnostics
Abstract: Lithium-Ion battery (LIB) on-line monitoring based on the extended equivalent circuit model (ECM) has received considerable attention. However, up to now, only linear observability analysis has been studied. The main purpose of this paper is to derive observability conditions for the extended ECM. Firstly, the battery ECM is decomposed into two sub-models mathematically, because there is no mutual coupling relationship between the battery states. Then, for the nonlinear extended one, an observability analysis is conducted. It shows that the observability necessary conditions depend on the battery current, the initial value of the battery capacity and the square of the derivative of the open circuit voltage (OCV) with respect to the state of charge (SOC). Numerical simulations have been conducted and have successfully assessed these conditions.

Keywords: Autonomous systems, Automotive control, Multi-agent systems
Abstract: We apply deep reinforcement learning to automated driving on highways. We propose a novel, simple framework with improved performance with respect to the state of the art. When implementing our algorithm on multi-lane highway scenarios, after the training phase, we observe via numerical simulations that the vehicles are able to avoid collisions and to reach their respective destination lanes with very high probability.

Keywords: Autonomous systems, Intelligent transportation systems, Neural networks
Abstract: Accurate and real-time trajectory prediction of traffic participants is important in autonomous driving systems, especially for decision making and risk assessment. Existing models such as physics-based and maneuver-based models are mainly used for short-term prediction. Deep-learning-based methods have been applied as novel alternatives for trajectory prediction. This problem can be viewed as a sequence generation task, where the future trajectory of vehicles is predicted based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, especially Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), in this paper an approach that combine LSTM for driving sequences classification and GRU for trajectory prediction is proposed. The obtained experimental results show the effectiveness of the proposed approach.

Keywords: Biomedical engineering
Abstract: In this article, the challenge of identifying between Essential and Parkinson's tremor is addressed. To this goal, a clinical analysis was performed, where a number of volunteers including Essential and Parkinson's tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately identify between different tremor types.

Keywords: Optimisation, Robotics
Abstract: This paper introduces a new hybrid PAC RL algorithm for MDPs, which intelligently maintains favorable features of its parents. The DDQ algorithm, integrates model-free and model-based learning approaches, preserving some advantages from both. A PAC analysis of the DDQ algorithm is presented and its sample complexity is explicitly bounded. Numerical results from a small-scale example motivated by work on human-robot interaction models corroborates the theoretical predictions on sample complexity.

Keywords: Predictive control, Nonlinear control, Neural networks
Abstract: This work proposes resilient operation strategies for nonlinear processes that are vulnerable to targeted cyberattacks, as well as detection and handling of standard types of cyber-attacks. Working with a general class of nonlinear systems, a modified Lyapunov-based Economic Model Predictive Controller (LEMPC) using combined closed-loop and open-loop control action implementation schemes is proposed to optimize economic benefits in a time-varying manner while maintaining closed-loop process stability. Although sensor measurements may be vulnerable to cyber-attacks, the proposed controller design and operation strategy ensure that the process will maintain stability and stay resilient against particular types of destabilizing cyber-attacks. Data-based cyber-attack detectors are developed using sensor data via machine-learning methods, and these detectors are periodically activated and applied online in the context of process operation. Using a continuously stirred tank reactor example, simulation results demonstrate the effectiveness of the resilient control and detection strategy in maintaining stable and economically optimal operation in the presence of cyber-attacks.

Keywords: Robotics
Abstract: This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology.

Keywords: Fault diagnosis, Signal processing, Aerospace control
Abstract: This paper presents an analytical redundancy approach for fault detection on a simulated hovering helicopter. For this purpose, a standard state-observer fault-detection technique is extended to adopt a commensurate fractional-order model. The results are favorably compared with those of an integer-order state observer, in terms of after-fault residue amplification and area under the receiver operating characteristic (ROC) curve.

Keywords: Image processing, Biologically inspired systems, Event based systems
Abstract: In this paper, the results of experiments in which a high velocity movement is simulated using digital light processing methods (with a Digital Light Processing (DLP) video projector) are presented. The main aim is to simulate a fast movement in order to test optic flow (OF) sensors or event-based sensor more quickly and simply than with traditional methods based on moving wallpaper or LCD screen. To obtain an accurate movement resolution, specific pictures are generated and displayed by the DLP at a high frame rate. The results show that accurate projected movement speeds are obtained with velocities of up to 50m/s. Propositions to increase these results are discussed.

Keywords: Navigation, Guidance, Signal processing
Abstract: The demand for precise indoor positioning is increasing nowadays, since traditional methods, like GPS, cannot be used in indoor environments. Lots of technologies exist that can be used for such purposes, having their own advantages and disadvantages when it comes to positioning. However, it seems that UWB is the most favorable, having the possibility to achieve the best accuracy due to its spectral features. Although, in the past years several companies appeared as a UWB chip and antenna manufacturer providing out-of-the-box solutions, most of them only have chips or demonstration kits to purchase. The paper shows the development and testing of an UWB indoor positioning system using commercially available hardware and specific software elements in order to allow flexibility required for research purposes.

Keywords: Prognostics and diagnostics, Signal processing, Modelling and simulation
Abstract: This paper proposes a wavelet-packet approach for fault detection using subspace identification. The wavelet-packet decomposition tree is used to define the frequency subbands at which the plant models are created. The best decomposition tree is chosen by using a dynamic programming algorithm that achieves a trade-off between accuracy and parsimony. A recently published framework is adopted to allow for the identification of subband models with a subspace approach. The proposed technique is validated with experimental data acquired with a Quanser^TM 3-DOF laboratory helicopter and presents better results than those of a standard time-domain fault-detection technique.

Keywords: Signal processing, Optimisation, Algebraic and geometric methods
Abstract: In this paper, the anisotropy-based estimating problem for linear discrete time-varying multiplicative noises finite horizon systems is considered. We examine the linear discrete time-varying system with uncertainty in the measurement output. The anisotropy-based bounded real lemma is obtained in terms of studied system matrices and desired estimator matrices. As result, we have the system of linear matrix inequalities in terms of estimator matrices. The numerical example of anisotropy-based estimating problem solution for the LDTVFH system is illustrated by the graphs.

Keywords: Signal processing, Wireless sensor networks, Computing and communications
Abstract: We propose a direction finding method for multiple-input multiple-output (MIMO) radar by using sparse sensing in low computational cost. Since the targets are sparsely distributed in the space, we employ the compressive sensing technique to reduce the sampling rate. Based on the compressive sensing, we utilize the convex combination to approximate the real target parameters in MIMO radar. We formulate an optimization problem for sparse vector recovery and off-grid mismatch estimation, which involves four set of variables. We employ the alternating direction method of multipliers approach to fast solve this optimization problem. In each iteration of sparse recovery, four subproblems are alternately optimized over only one of four set of parameters where each subproblem has a closed-form solution. With the recovered sparse vector and the estimated off-grid mismatch, we develop a grid adjustment method to accurately resolve the directions of targets by iteratively deleting the redundant grid points. Numerical simulations indicate that the proposed method is able to achieve accurate signal recovery, improved estimation accuracy and reduced computational cost.

Keywords: Assistive technology, Energy efficient systems
Abstract: This paper proposes a hierarchical multilevel control strategy, adaptive on the cyclist characterization, for the energy management of a fleet of hybrid electric bikes (HEBs) in a free-floating sharing service. We pursuit the goals of independence from recharging stations and of cycling effort reduction by means of motor assistance. To achieve these goals we exploit the variability of cycling efficiency. The results are assessed in a simulation environment.

Keywords: Industrial automation, manufacturing, Neural networks
Abstract: Laser cutting of metals offers the advantage of high precision and accuracy. Dross attachment, measured as the length of the re-solidified material perpendicular to the surface, has definitely the highest impact on the overall process quality. Dross attachment is commonly judged by skilled technicians that evaluate the cut quality. Process parameters are optimized to maximize the cutting speed while keeping an acceptable level of dross attachment. However, in practice, increased levels of dross may occur due to different processing conditions. In this framework, a real-time dross attachment monitoring system is desired. Within the stream of vision based monitoring systems, in this work we use high frequency images generated by a precision camera, mounted on the laser head, to capture the cutting process light emission. A CNN-based classification system is developed, where captured images are fed into the trained network with the aim of automatically recognize if a predetermined dross attachment level is exceeded. To our best knowledge, this is the first work where a CNN is used for monitoring the quality of laser cutting process via dross attachment classification.

Keywords: Industrial automation, manufacturing, Petri nets, Modelling and simulation
Abstract: Business process re-engineering is crucial for manufacturing companies to improve their productivity and efficiency. The identification of the main criticalities affecting the production processes and the implementation of effective re-engineering solutions can significantly reduce the company losses. However, such actions can be unsuccessful if suitable preliminary investigations on the effectiveness of the solutions are not performed. This paper proposes an integrated process re-engineering technique that allows to: identify workflows via the Unified Modeling Language; model and simulate the business process via Colored Petri Nets (CPNs); detect bottlenecks and waste sources through the Value Stream Mapping tool; rank the impact of the detected criticalities via a mathematical formulation of the Genba-Shikumi lean philosophy; simulate the re-engineering actions and evaluate their effectiveness using the CPN model. The aim is to offer an intuitive tool for strategic decision making, deployable at a managerial level in a digital twin approach. The proposed technique is tested on a textile company located in Southern Italy, showing its effectiveness in removing inefficiencies and ensuring the continuous improvement of the production process.

Keywords: Petri nets, Modelling and simulation, Cyber-physical systems
Abstract: In the current work the development procedure (design, manufacture and assembly) of an electric vehicle is considered.. Uncertainties make difficult to follow initial time plan, so monitoring of the development procedure is necessary. To handle delays a method using Petri nets to model the tasks of the development procedure and their dependencies is introduced. The model is not used offline as a passive element but is connected and interacts with the physical system (development procedure). Based on the information exchange between physical and digital system, alternative ways to overcome delays are studied and the optimal solution is calculated, tested and applied. Results are provided according to different scenarios, in order to show the efficiency and applicability of the proposed method.

Keywords: Power systems and smart grid
Abstract: This paper presents an economic model predictive control approach for a linear microgrid model. The microgrid in grid-connected mode represents a medium-sized company building including storage systems, renewable energies and couplings between the electrical and heat energy system. Economic model predictive control together with Pareto optimization is applied to find suitable compromises between two competing objectives, i. e. monetary costs and thermal comfort. Using real-world data from 2018 and 2019, the model is simulated with auto-detection of the Pareto solution which is closest to the Utopia point. The results show that the Pareto optimization can either be used in real-time control of the microgrid, or to obtain suitable weights from long term simulations. Both approaches result in significant cost reductions.

Keywords: Power systems and smart grid, Nonlinear systems, Nonlinear control
Abstract: A novel droop controller for DC microgrid systems, consisting of multiple paralleled sources feeding a constant power load (CPL), is proposed to achieve the desired voltage regulation and accurate load power distribution, while ensuring an overvoltage protection for each source. CPLs are well-known to exhibit negative impedance characteristic due to their nonlinear behaviour, which may cause instability to a DC microgrid if the necessary impedance inequality criteria are not satisfied. In this paper, a new droop control scheme is proposed to limit the voltage of each source below a desired bound, achieve tight voltage regulation and power sharing, and guarantee closed-loop system stability with the existence of a CPL. The upper limit of the voltage of each source is rigorously proven using ultimate boundedness theory, while after a suitable manipulation of the admittance matrix of the microgrid, analytic conditions of stability are obtained to guide the control parameter design. To validate the theoretical design and analysis, a detailed simulation is performed of a DC microgrid equipped with the proposed controller