Keywords: Fault diagnosis, Prognostics and diagnostics, Sampled-data systems
Abstract: The Fast Fourier Transform is the unique tool enabling to engage the frequency domain analysis in detection and isolation of periodic disturbances in industrial processes containing rotating elements. Nevertheless, there exist particular problems where the time-domain examination of oscillations can provide equally or more accurate results with less effort. Specific data sampling, filtering and process modelling are introduced in the paper with the aim to classify sources of oscillations in the process of cold rolling. Although the FFT may not be part of the method itself, its use allows to illustrate the presented research.

Keywords: Fault diagnosis, Cyber-physical systems, Prognostics and diagnostics
Abstract: AI-enabled mechanisms are deployed to guard controlled systems against sensor anomalies. We explore a two-level architecture design in which a low-level feedback controller of a linear system uses measurements from one or more potentially unreliable sensors. These observations are prone both to sensor noise but unknown additive faults. Our proposed, high-level, guard mechanism consists of a Reinforcement Learning (RL) agent that monitors available vitals of the system. In the event of a fault on the sensor components, the RL agent automatically detects, estimates the fault, localizes and takes action to cancel the fault. In addition, we develop design methodologies for efficient training of the RL agent that take advantage of system dynamics and sensor fusion schemes. We show that the associated training cost functions can be designed so that their optimal policy achieves efficient of arbitrary constant or piece-wise constant sensor faults. To illustrate our theoretical results, we consider a linearized version of a chemical process with multiple sensors, controlled by a Linear Quadratic Gaussian (LQG) Servo-Controller with Integral Action. Our simulations show that the RL-agent is successful in localizing the faulty sensors and mitigating the effects of faults in an online fashion.

Keywords: Fault diagnosis
Abstract: This paper proposes an unknown input root mean square cubature Kalman filter algorithm, which is applied to the fault diagnosis of nonlinear systems with unknown input. Firstly, a standard linear regression equation with unknown input is constructed, and orthogonal trigonometric decomposition is combined to solve the equation to improve the estimation accuracy of unknown input. In addition, in order to improve the numerical stability of algorithm, the root mean square algorithm is introduced into the error covariance matrix calculated from the unknown input estimation and state estimation results. Secondly, the root mean square value of the sliding window of residual obtained from the difference between the measured value and the estimated value is computed to judge whether the actuator has a fault. The generalized regression neural network is used for fault identification. Finally, a single link manipulator system is taken for simulation verification.

Keywords: Fault diagnosis, Nonlinear systems, Discrete-event systems
Abstract: In this paper, zonotopic fault detection methodology is proposed for a class of discrete-time Linear Parameter Varying (LPV) systems with sensor faults. The disturbances and measurement noise are assumed to be unknown but bounded by zonotope. First, a fault detection observer is designed based on L ∞ performance to attenuate the effects of the uncertainties and to improve the accuracy of the proposed residual framers. Then, the fault sensitivity is taken into account by measuring H − performance and zonotopic residual evaluation is presented. Finally, the effectiveness of the proposed method is demonstrated by a numerical example

Keywords: Fault diagnosis, System identification, Hybrid systems
Abstract: The global trend of increasing the relevance of renewable energy in the power grid is likely to remain in the following years. As wind power is a relevant renewable energy source, wind capacity generation has increased considerably. Consequently, the costs of maintenance of wind turbines increase as well. Therefore, the development of Structural Health Monitoring (SHM) systems is important since they can detect defects as early as possible, reduce the wind turbine's downtime, and maintain the efficiency of the generation sites, which reduces losses. In this work, we use a public wind turbine blades benchmarking dataset to build a model that predicts and classifies fault scenarios. The dataset presents cases considering various fault events under different climate conditions. First, it is analyzed which of the sensors best captures the dynamics difference when there is a fail presence on the wind turbine blade. Then, we extract features from the sensor signal through Principal Component Analysis (PCA) and system identification, an Auto-Regressive Moving-Average (ARMAX) model. After, the addition of a fault classification module that uses Machine Learning classification algorithms completes the development of the SHM systems.

Keywords: System identification, Networked systems, Distributed systems
Abstract: We consider the problem of distributed Kalman filtering for sensor networks in the case there is a limit in data transmission and there is model uncertainty. More precisely, we propose a distributed filtering strategy with event-triggered communication in which the state estimators are computed according to the least favorable model. The latter belongs to a ball (in Kullback-Leibler topology) about the nominal model. We also present a preliminary numerical example in order to test the performance of the proposed strategy.

Keywords: Networked systems, Spectral estimation, Swarms
Abstract: Centrality measures evaluate the importance of vertices in a graph. In a multi-agent framework i.e. robots swarm, computing these indicators may be useful in identifying a leader. As a first step, the paper deals with a centrality measure based on the analysis of the eigenvector associated with the largest eigenvalue of the Laplacian matrix. For the studied class of graphs, this centrality measure highlights the most connected vertex which is also associated with the largest binding energy. The results are established for complement of trees. For these highly connected graphs, the most popular centrality measures make hard to distinguish an important vertex. On the contrary, the proposed indicator distinctly highlights this vertex. As a second step, for a network of autonomous agents, the leader identification problem based on this centrality measure is solved in a decentralized way. All the agents cooperate to appoint their leader. The calculations are conducted in the frequency domain and are based on maximum-consensus functions

Keywords: Power systems and smart grid, Networked systems, Nonlinear control
Abstract: In this paper, the problem of frequency control in multimachine power networks is investigated in a distributed manner. Taking as an example a cyber-physical architecture of the IEEE 68 bus, 16 machine power system, a new distributed bounded integral control (DiBIC) scheme is proposed to achieve frequency restoration to the rated value and power sharing among the 16 generators with only neighbour-to-neighbour communication, while maintaining the mechanical torque of each generator within predefined limits. The boundedness of the mechanical torque inputs is analytically proven independently of the distributed control tasks and without the need for saturation mechanisms that might lead the power system to instability. To verify the proposed DiBIC performance, the IEEE 68 bus network is simulated under a change of the load demand and tested under both the proposed and the conventional distributed integral control with saturation.

Keywords: Networked systems, Decentralized control, Distributed systems
Abstract: We consider the problem of distributed analysis and control synthesis to verify and ensure properties like stability and dissipativity of a large-scale networked system comprised of linear subsystems interconnected in an arbitrary topology. In particular, we design systematic networked system analysis and control synthesis processes that can be executed in a distributed manner at the subsystem level with minimal information sharing among the subsystems. Compared to recent work in the literature, we consider a substantially more generalized problem setup and develop distributed processes to verify and ensure a broader range of properties. We show that optimizing subsystems' indexing scheme used in such distributed processes can substantially reduce the required information-sharing sessions between subsystems. We also show that sharing information among the neighboring subsystems is sufficient for the proposed distributed processes in some network topologies. Moreover, the proposed distributed processes are compositional and thus allow them to conveniently and efficiently handle situations where new subsystems are being added to an existing network. We also provide significant insights into our approach so that it can be quickly adopted to verify and ensure properties beyond the stability and dissipativity of networked systems. Finally, we provide a numerical example to demonstrate the proposed distributed processes and highlight our contributions.

Keywords: Nonlinear systems, Networked systems, Autonomous systems
Abstract: This paper focuses on finite time and fixed time consensus protocols for discrete-time networks. Specifically, we build on the theory of finite time semistability for discrete-time dynamical systems to develop Lyapunov theorems for fixed time semistability. These results are then used to develop a framework for designing finite and fixed time semistable consensus protocols for discrete dynamical networks that achieve multiagent coordination tasks in finite and fixed time.

Keywords: Cyber-physical systems, Distributed systems, Disturbance rejection
Abstract: The greater availability and accessibility of IoT sensors, data acquisition systems and computer networks, led to the development of distributed control strategies aimed at monitoring and securing of Cyber-Physical Systems (CPS). In this paper, we propose a distributed asynchronous algorithm for the Secure State Reconstruction (SSR) which relies on a gossip-based procedure to let a network of agents reach a shared estimate of the process state based on measurements taken by the different agents at different time instants. In particular, the procedure aims to reduce measurement noise and provide resilience in the event of cyber attacks that modify the readings of agents or cause their disconnection, combined with the possibility of providing new virtual measurements in real-time. In addition, the gossip-based pair connection is randomly established and subsequently destroyed, making it difficult for an attacker to intercept the information exchange. In detail, we first analytically demonstrate the convergence and correctness of the proposed algorithm, and then we provide a simulation campaign to demonstrate its effectiveness.

Keywords: Cyber-physical systems, Networked systems, Power systems and smart grid
Abstract: This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm provides as a result an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a power network subject to D-LAAs modelling approach suitable for the designed robust control protection scheme. The robust control protection scheme criteria is identified with the technological application. The paper proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases, and different numerical simulations for the validation of the approach on large networks.

Keywords: Mechatronic systems, Cyber-physical systems, Modelling and simulation
Abstract: This paper presents a digital twin infrastructure developed to study and test the buoyancy set-up of a diver propulsion vehicle (DPV) with and without payloads prior to the underwater survey. In order to obtain the final software simulator, MATLAB/Simulink (for physical and mathematical models) was connected with Unity (for robot and environment visualization and navigation). A user interface is also presented to simulate the model directly and guide the user in adding objects to the DPV and in running the code to recalculate the fundamental parameters of the model. This work originated from the DiveSafe European project (G.A. EASME/EMFF/2017/ 1.2.1.12/SI/02/ SI2.789635) and the need to study the proper buoyancy for the equipped systems involved. The system was verified and validated through case studies designed to test the behavior of the virtual DPV.

Keywords: Networked systems, Cyber-physical systems, Aerospace control
Abstract: This paper is concerned with state tracking as well as reference tracking of noisy nonlinear dynamic systems over Additive White Gaussian Noise (AWGN) channel, which is sub- ject to transmission noise imperfection and transmission power constraint. In order to address these problems, in this paper we implement a suitable linearization method. Using this method, we linearize the nonlinear dynamic system around working points and for linearized systems, we present proper encoder and decoder for tracking the state trajectory of nonlinear dynamic systems at the end of communication link when sensor measurements are sent through the AWGN channel subject to imperfection and constraint. The satisfactory performance of the proposed state and reference tracking techniques are illus- trated via computer simulations by applying these techniques on the unicycle model, which is an abstract representation for the nonlinear dynamics of autonomous vehicles.

Keywords: Cyber-physical systems, Computational intelligence, Image processing
Abstract: Agricultural cyber-physical systems (ACPS) are an ever-increasing sector that affects the quality and quantity of agricultural products as the population increases rapidly. Maize, also known as 'corn,' is one of the world's old food crops, consumed every part of Bharat with 1.4 billion masses across the globe. But a disease, whether on seeds, leaves, or other parts of a crop plant, poses a significant risk to food security—for example, a Maize leaf experiences three diseases- blight, common rust, and gray leaf spot. Early detection and correct identification of these diseases can help restrict the spread of infection and ensure crop quality for long-term health. This paper proposes a deep convolutional neural network (DCNN) framework for Maize leaves named "MDCNN" that detects these diseases. The proposed MDCNN model undergoes training and is tuned to detect four prevalent classes of the conditions. The proposed model exercises a voluminous dataset of the diseases. Experimental results demonstrate that the proposed model achieves a training and test accuracy up to 95.51% and 99.54%, respectively. Furthermore, the precision, recall, and f1-score metrics achieved by the proposed approach are in the range of 89-100%, 87-100%, 92-100%, respectively. Thus, the MDCNN outperforms many existing methods and delivers a superior disease control solution for Maize leaf diseases.

Keywords: Robotics, Robust control, Aerospace control
Abstract: The rapid increase in satellites and space debris mandates advanced capabilities for on-orbit operations. The hostile-to-human environment and the required high accuracy and robustness of on-orbit operations render Space Manipulator Systems (SMS) the appropriate choice. This work proposes an easily applicable, computationally inexpensive, nonlinear, and robust Cartesian control law for spatial Free- Floating SMS (FFSMS). The controller consists of two fundamental parts. The first is a Model-Based (MB) controller, which linearizes the system and guarantees prescribed performance. The second is a Model Predictive Controller (MPC), which integrates the model and provides optimal performance with parametric uncertainty, noise, and disturbances compensation. Input and output constraints are integrated into the latter to improve its performance. Numerical simulations for a planar model using Matlab/Simulink and MSC Adams highlight the MB/MPC’s increased accuracy in comparison to a regular MB/PID controller, during a task that requires moving a captured object in the presence of parametric uncertainty, disturbances, and sensor noise. Monte-Carlo simulations substantiate the higher accuracy achieved by the MB/MPC.

Keywords: Robotics, Predictive control
Abstract: We present a novel real-time motion generation approach for mobile manipulators which maintains balance even when the robot is called to execute aggressive motions. The proposed approach is based on Nonlinear Model Predictive Control (NMPC) and uses the robot full dynamics as prediction model. Robot balance is maintained by enforcing a constraint that restricts the feasible set of robot motions to those generating non-negative moments around the edges of the support polygon. This balance constraint, inherently nonlinear, is linearized using the NMPC solution of the previous iteration. In this way we facilitate the solution of the NMPC and we achieve real-time performance without compromising robot safety. We validate our approach in scenarios of increasing difficulty and compare its performance with two other methods from the literature. The simulation results show that our method can generate motions that maintain balance in challenging situations where the other techniques fail.

Keywords: Robotics, Modelling and simulation, Mechatronic systems
Abstract: In this paper we present the modelling and analysis of a novel parallel manipulator for research in laparoscopic robotic surgery. The robot's configuration is based on the parallel linking of two Delta robots. We analyse the point and velocity kinematics of the platform, and also show that it can accommodate a dynamic remote centre of motion, tailored to the specific needs of each task. Finally, we analyse the platform's reachable and orientation workspaces and show that it meets the demands of various tasks in laparoscopic surgery.

Keywords: Robotics, Image processing, Neural networks
Abstract: Robust and energy-efficient robot planning is of utmost importance for mobile robots since the dynamic changes of the environment entail robotic agents with high adaptation capacities, so as to excel in their tasks. In this work, we introduce a hybrid spiking and deep neural network architecture for actor-critic control of a 6-DOF robot arm. Our method firstly involves autonomous object detection via active vision exploration and thereafter, the entire hybrid architecture is described. In specific, the actor utilises an integrated-and-fire model for action generation, while the critic a deep neural one for action evaluation. Lastly, the benefits of this approach in terms of energy efficiency are extensively discussed.

Keywords: Robotics, Industrial automation, manufacturing, Optimisation
Abstract: Research on kinematic calibration of industrial robots has focused on applying different measurement instruments into open- and closed-loop approaches and optimising calibration configurations through various cost functions. Such ways are either expensive or time-consuming. This work presents essential steps towards realising quasi-static kinematic calibration of industrial manipulators. This approach employs measurement data from a quasi-static measurement instead of a static one to identify the model parameters and has the potential of considerably reducing the measurement phase time during calibration. The focus lies on the technological challenges needed to achieve a successful quasi-static kinematic calibration, such as the trajectory generation, the measurement instrument and the controller data synchronisation. A case study assess the data obtained from a quasi-static kinematic measurement with a robot/tracker configuration of 100 mm/s and 100 Hz. The average positioning accuracy is similar for the static and the quasi-static measurement. The time for the quasi-static trajectory is reduced to almost one-third of the static trajectory time without considering the setup time.

Keywords: Fault tolerant control, Feedback stabilization, Linear systems
Abstract: This paper deals with an integrated design interval observer-based Fault Tolerant Control (FTC) for linear time invariant (LTI) system subject to the uncertain modeling, actuator fault and external disturbances. Under the assumption that uncertainties and disturbances are unknown but bounded with priori known bounds, a design method for obtaining Proportional Integral (PI) interval observer that provides guaranteed lower and upper bounds of the state as well as faults is considered. Based on the estimated information, FTC controller is designed to ensure robust stability of the closed-loop system. Sufficient stability conditions with H_infty performance are expressed in terms of Linear Matrix Inequalities. An application to vehicle lateral dynamics is considered to show the effectiveness of the proposed algorithms.

Keywords: Fault tolerant control, Networked systems, Predictive control
Abstract: This paper presents an economic reliability-aware Model Predictive Control (MPC) approach for the Prognostics and Health Management (PHM) of generalized flow-based networks. The main enhancement with respect to some existing approaches relies on the integration of the network reliability model obtained from a Bayesian Network. The goal is that the controller is able to optimally manage the supply taking into consideration the distribution of the control effort, in order to extend the life of the actuators by delaying as much as possible the network reliability decay. But, it also considers an optimal inventory replenishment policy based on a desired risk acceptability level, leading to the availability of safety stocks for unexpected excess demand in networks. The proposed implementation is illustrated with a real case study corresponding to an aggregate model of the Drinking Water transport Network (DWN) of Barcelona.

Keywords: Fault tolerant control, Fault diagnosis, Autonomous systems
Abstract: Logic-based formal models of robot environments are often used to aid the generation and verification of robotic plans. They are however often simplified and rather abstract compared to the real world that the robot acts in. This can lead to considerable discrepancies between the behavior of the formal model and that of physics-based simulation engines. These discrepancies are not always apparent to the designer. In this paper we propose a new methodology to make these discrepancies explicit by combining formal verification and simulation. Our approach is able to find relevant discrepancies, while only requiring a small number of simulations.

Keywords: Cyber-physical systems, Optimisation, Fault tolerant control
Abstract: Security attacks on sensor data can deceive a control system and force the physical plant to reach an unwanted and potentially dangerous state. Therefore, attack detection mechanisms are employed in cyber-physical control systems to detect ongoing attacks, the most prominent one being a threshold-based anomaly detection method called CUSUM. Literature defines the maximum impact of stealth attacks as the maximum deviation in the plant's state that an undetectable attack can introduce, and formulates it as an optimization problem. This paper proposes an optimization-based attack with different saturation models, and it investigates how the attack duration significantly affects the impact of the attack on the state of the plant. We show that more dangerous attacks can be discovered when allowing saturation of the control system actuators. The proposed approach is compared with the geometric attack, showing how longer attack durations can lead to a greater impact of the attack while keeping the attack stealthy.

Keywords: Distributed systems, Predictive control, Energy efficient systems
Abstract: Model predictive control (MPC) is a promising approach to reduce energy usage in buildings and provide grid flexibility. However, MPC in buildings requires a high modeling effort and struggles with reliability and scalability. Distributed control architectures can help to alleviate these problems. Existing work for distributed building MPC mainly focuses on the consumer side, neglecting the producer dynamics. In this work, we investigate a distributed MPC scheme based on the alternating direction method of multipliers (ADMM), considering the dynamics of producers and consumers simultaneously. The control agents employ nonlinear plant models and are directly coupled through their states. Consequently, a distributed, non-convex optimization problem needs to be solved. We test the distributed control algorithm on an energy system consisting of two thermal zones, a stratified hot water tank, a boiler, and a CHP. In a simulation, we demonstrate that the closed-loop system can be stabilized using the distributed MPC scheme, given a sufficient number of iterations.

Keywords: Distributed systems, Multi-agent systems, Networked systems
Abstract: In this paper we address the problem of distributively tracking the upper or lower bound of n time-varying signals in finite-time. In detail, each agent has access to a time-varying exogenous signal, which may encode the evolution of a physical phenomenon. All the agents are required to follow the upper or lower bound value among such output signals in a distributed fashion. We provide a protocol to solve the above problem in the case of networked agents and undirected communication, together with formal proof of convergence and estimation of an upperbound of the convergence time. We corroborate the protocol via numerical validations in a precision farming setting.

Keywords: Distributed systems, Predictive control, Optimisation
Abstract: This paper presents a dual decomposition-based distributed optimization algorithm and applies it to distributed model predictive control (DMPC) problems. The considered DMPC problems are coupled through shared limited resources. Lagrangian duality can be used to decompose an MPC problem, so that each subsystem can compute its individual resource utilization, without sharing information, such as dynamics or constraints, with the other subsystems. The feasibility of the central problem is ensured by the coordination of the subproblems through dual variables which can be interpreted as prices on the shared limited resources. The proposed coordination algorithm makes efficient use of information collected from previous iterations by performing a quadratic approximation of the dual function of the central MPC problem. Aggressive update steps of the dual variables are prevented through a covariance-based step size constraint. The nonsmoothness encountered in dual optimization problems is addressed by the construction of cutting planes, similar to bundle methods for nonsmooth optimization. The cutting planes ensure that the updated dual variables do not lie outside the range of validity of the dual approximation. The proposed algorithm is evaluated on a two-tank system and compared to the standard subgradient method. The results show that the rate of convergence towards the centralized solution can be significantly improved while still preserving privacy between the subsystems through limited information exchange.

Keywords: Distributed systems, Optimisation, Renewable energy and sustainability
Abstract: This paper presents a mixed-integer linear programming-based optimization model for simultaneous optimal load control and scheduling of distributed systems coupled through their energy consumptions. The subsystems are able to adjust their energy consumption during the execution of a task and aim at minimizing their completion time and energy cost. The overall problem is solved in a distributed fashion, where each subsystem optimizes its individual operation without sharing sensitive information. To this end, dual decomposition is employed and a new algorithm to update the dual variables is presented. It relies on a transformation of the gradient of the quadratically approximated dual function and the subsequent solution of a regression problem. The proposed algorithm makes efficient use of information collected in previous iterations. The solution obtained from the distributed optimization of the subsystems is compared to both a decentral and a system-wide solution, showing that the distributed solution lies close to the global optimum of the process.

Keywords: Distributed systems, Networked systems, Cyber-physical systems
Abstract: Distributed average consensus is a fundamental feature of multi-agents systems; yet, in several cases agents are reluctant to disclose their initial conditions, e.g., due to their sensitivity about private data. Consequently, ensuring the privacy of such information against honest but curious neighbors becomes a mandatory necessity. In this paper we propose to implement a privacy-preserving consensus strategy that exploits, for this purpose, unpredictable chaotic phenomena, such as the trend of variables in a Chua oscillator. The initial conditions are then split into two fragments, one of which always remains hidden in the node, while the other is exchanged after undergoing oscillator-dependent manipulation, adding an extra layer of security to what is exchanged over the network. In this way, the combination of the two fragments converges to the average of the true initial conditions of each node. The paper is complemented by a simulation campaign aimed at numerically demonstrating the effectiveness of the proposed approach.

Keywords: Automotive control, Adaptive control, Robust control
Abstract: This study represents the integrated comfort-oriented velocity design, tracking control, and adaptive semi-active suspension control method. The velocity design approach is based on the ISO 2631-1 standard, while the proposed velocity tracking control method is based on the LPV control architecture. A road adaptive semi-active suspension control method, where a trade-off between vehicle stability and driving comfort is accomplishable to achieve desirable performance results at different road profiles and velocities. The trade-off is accomplishable due to flexibility and online reconfigurability of the LPV control method by online modification of scheduling variables. The design is based on the performance index, road profile, and the designed velocity of the velocity. The velocity designer, velocity tracking controller, and adaptive suspension controller have been integrated and simulated in the TruckSim environment to show the operation of the proposed method.

Keywords: Automotive control, Autonomous systems, Intelligent transportation systems
Abstract: The appearance of autonomous vehicles (AVs) in transportation has increased the attention of the scientific community to develop modern solutions for the control design of AVs in different traffic scenarios. In this paper a control method is proposed for the coordination of autonomous vehicles in roundabout scenarios. For collision avoidance and minimization of traveling time, a Model Predictive Control (MPC) with a centralized controller is introduced to calculate the traveling times of the vehicles. A presented algorithm determines velocity profiles for safety reasons and for the reduction of possible congestion. The operation of the proposed MPC method is tested and demonstrated in CarSim simulation environment.

Keywords: Automotive control, Predictive control, Autonomous systems
Abstract: This paper presents a control approach allowing a tight integration of several driver assistance functions in automotive vehicles, including front steering, differential braking and autonomous emergency braking. The goal is to support a more effective collision avoidance with necessary emergency maneuvers during automated driving, while avoiding situations with a high risk of undesired vehicle instability due to separated steering and braking actions. The approach adopts a Model Predictive Control (MPC) based methodology using a set of performance indexes calculated according to the actual time to collision, braking and steering capabilities. The effectiveness of the proposed methodology is evaluated in a simulation on MATLAB/Simulink.

Keywords: Automotive control, Predictive control, Robust control
Abstract: In this paper, we propose an optimal online tuning scheme for design-related scheduling parameters of adaptive Linear Parameter Varying (LPV) control systems. Specifically, the method is conceived within the Model Predictive Control (MPC) framework, which we demonstrate to ensure an input-to-state stable closed-loop and a recursively feasible optimisation program. The major advantage of the proposed solution is that it automatically determines the LPV scheduling parameters online, without the need to for the designer to develop any scheduling function (which is often a repetitive and obscure task). Moreover, it offers a direct and simple tuning procedure, able to directly incorporate multi-objective performance goals into a single quadratic cost. The proposed method is tested for an LPV Advanced Driver Assistance System, showing enhanced performances when compared to state-of-the-art methods based on nonlinear scheduling functions.

Keywords: Automotive control, Predictive control, Formation control
Abstract: Vehicle-to-vehicle communication represents the main capability of vehicles that stays at the base of cooperative control solutions. Vehicle groups, e.g., platoons, exchange information about velocities to improve the performances of the traffic (i.e., reducing fuel consumption/emissions, improving the quality of the travel). Due to the importance of communication in literature, manifold types of communication topologies were proposed for various scenarios. In this paper, a study of the performances of the communication topologies is performed, starting from a cooperative adaptive cruise control (CACC) algorithm used to control the vehicles from a platoon in a specific scenario, four communication topologies were tested, and numerical analysis was performed to point out the advantages of each topology.

Keywords: Robotics, Autonomous systems, Nonlinear systems
Abstract: In the case of the control synthesis of the autonomous robot precise motions in production processes, or autonomous micro/nano robot motions in drug delivery, one have to use a related Hamiltonian. It is assumed that the autonomous robot (or micro/nano robot) motions are in a multipotential field. In order to simplify control process, the concept of the external linearization is used. In that case, the nonlinear control in the closed loop with the nonlinear canonical differential equations of the autonomous robot (or micro/nano robot) motion is resulting in the linear behavior of the whole system. Therefore, the linear control algorithms can be applied. This is the main contribution of the paper. The control process is calculated in the combination of the electromagnetic and gravitational fields. In that case, the autonomous robot (or micro/nano robot) moves towards a target with limited information about the environment. The main point of these algorithms is to minimize the use of the outer perimeter of an obstacle, which also minimizes total path length taken by the autonomous robot (or micro/nano robot).

Keywords: Robotics, Biologically inspired systems, Modelling and simulation
Abstract: Bipedal walking is one of the most important hallmarks of human that robots have been trying to mimic for many decades. Although previous control methodologies have achieved robot walking on some terrains, there is a need for a framework allowing stable and robust locomotion over a wide range of compliant surfaces. This work proposes a novel biomechanics-inspired controller that adjusts the stiffness of the legs in support for robust and dynamic bipedal locomotion over compliant terrains. First, the 3D Dual-SLIP model is extended to support for the first time locomotion over compliant surfaces with variable stiffness and damping parameters. Then, the proposed controller is compared to a Linear-Quadratic Regulator (LQR) controller, in terms of robustness on stepping on soft terrain. The LQR controller is shown to be robust only up to a moderate ground stiffness level of 200 kN/m, while it fails in lower stiffness levels. On the contrary, the proposed controller can produce stable gait in stiffness levels as low as 30 kN/m, which results in a vertical ground penetration of the leg that is deeper than 10% of its rest length. The proposed framework could advance the field of bipedal walking, by generating stable walking trajectories for a wide range of compliant terrains useful for the control of bipeds and humanoids, as well as by improving controllers for prosthetic devices with tunable stiffness.

Keywords: Distributed systems, Robotics, Autonomous systems
Abstract: Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

Keywords: Discrete-event systems, Robust control, Industrial automation, manufacturing
Abstract: Automated Guided Vehicles (AGVs) are increasingly popular and bring many industrial benefits. However, when a number of AGVs autonomously execute their itineraries, it is possible for two or more AGVs to prevent each other from completing their tasks and cause a deadlock from where the system cannot progress. One way that companies try to avoid this is to, based on simulations, generate deadlock avoidance rules (DA-rules) that determine for different scenarios how the AGVs should behave. This paper presents an application of translating such DA-rules to extended finite-state automata and then to formally verify if the rules actually do avoid deadlocks. This is done by using information of an existing system setup where there are two major types of DA-rules. Both of these can be modeled as automata with guards and actions that prevent a transition from occurring if associated conditions are not fulfilled. These guards are generated automatically for all the DA-rules corresponding to the current itineraries. For a chosen itinerary a complete automaton is generated, as well as automata representing the DA-rules. It is shown that the existing DA-rules do not manage to remove all deadlocks in all cases. Even worse, it is shown that the DA-rules can lead to a fully blocking system, even though a deadlock-free solution does exist, as can be shown by computing a supervisor for the system without the DA-rules.

Keywords: Modelling and simulation, Nonlinear systems, Robotics
Abstract: Legged locomotion has been a widespread daily activity of animals for millions of years, and many of its subtleties are founded on imposed conditions for survival. Agile movement in environments built for human locomotion could be achieved by extracting and implementing key aspects of legged locomotion in artificial creations. This work proposes a novel high-level control system module for dynamic quadruped robot locomotion, integrating the rhythmic developing capabilities of CPGs with foot trajectory generation based on Bezier curves. The proposed system utilizes CPG output signals as driving parameters for a foot trajectory generator based on Bezier curves} and is built using a Matlab/Simulink simulated environment, with tests being carried out to validate its quadruped locomotion aptitude with regards to limit-cycle convergence and establishment of synchronized and stable phase shifts corresponding to desired gaits. The results point toward a sound performance of the proposed strategies, showing great promise regarding its locomotion capabilities. Lastly, additions to enhance the system's effectiveness and enable its use in a complete locomotion control system are suggested to be further explored in future works.

Keywords: Petri nets, Discrete-event systems, Optimisation
Abstract: Opacity is a property of discrete event systems (DES) that is related to the possibility of hiding a secret from external observers (the intruders). When the secret is the initial state of the system, the related opacity problem is referred to as Initial State Opacity (ISO). A sufficient condition to check ISO by solving Integer Linear Programming problems is given in this paper. Such a condition exploits the algebraic representation of Petri nets and a structural one of its behavior in terms of minimal support T-invariants. The effectiveness of the proposed approach is shown by means of examples.

Keywords: Hybrid systems, Disturbance rejection, Algebraic and geometric methods
Abstract: In this paper, we consider the problem of decoupling the output of a given system from a disturbance input by means of a dynamic output feedback for the class of linear impulsive systems with periodic jumps. By introducing a suitable notion of hybrid (C,A,B)-pair, the solvability of the problem is completely characterized by means of a structural condition. The additional requirement of stability, in a suitable sense, of the closed-loop system is also considered and a sufficient condition for the solution of the problem in this case is found. Viable algorithmic procedures for constructing solutions, if any exist, are also devised.

Keywords: Switching systems, Fuzzy logic and fuzzy control
Abstract: The paper deals with the interval switching observers design for nonlinear switched positive systems. The systems are represented by the Takagi-Sugeno fuzzy switched models, with measurable premise variables. Proposed design conditions for the interval switching observer structure are formulated via linear matrix inequalities to ensure non-negative state estimation of the considered class of fuzzy switched systems. Under such conditions the proposed method allows to compute the lower and upper bounds of the system state assuming that Lipschitz function is positive and the system disturbance is positive and bounded. The properties of the proposed approach are illustrated by a numerical example.

Keywords: Discrete-event systems, Event based systems, Hybrid systems
Abstract: In this paper we address control design of nondeterministic finite state systems with reach-avoid specifications. A general class of controllers is considered, which combines feedforward and output feedback schemes. Results proposed here extend those of [1] in two directions: first, here we consider reach avoid-specifications instead of simply reachability specifications; second, we propose algorithms exhibiting better time computational complexity than those given in [1].

Keywords: Energy efficient systems, Hybrid systems, Power systems and smart grid
Abstract: Battery electric vehicles (BEV) initially appeared as a promising solution against climatic disasters due to petroleum vehicles and the continuous growth in energy demand for road transport. However, their weak autonomy combined with too high a cost slowed down their development in the world market. However, a second transition to multi-source electric vehicles which consist of a set of Energy Storage Systems (ESSs), may be possible solutions to improve the vehicle's autonomy and the duration of battery life by significantly reducing polluting emissions to the environment. In fact, this paper presents a hybrid SSE which is mainly based on High energy lithium-ion (Li-ion) batteries and High power batteries for powering an all-electric vehicle. This hybridization of a main High energy Li-ion battery source with the secondary High power batteries source adds greater HEV autonomy but it increases the complexity of the Energy Management System (EMS). In this work, the main objective is designed to simulate the strategies based on deterministic rules like the filtering method (MF) and the Limitation method (ML) for running electric vehicle with hybrid source in real time. Strategies ML and MF were chosen in the first place thanks to their simplicity in time integration real but unfortunately these techniques have no control over the behavior of the High power batteries during the rolling cycle. For this an improvement of these three strategies was introduced by integrating a technique of control of the state of charge "Control SOC" of this secondary source in order to follow the behavior of the High power batteries along the conduct of the driver in order to ensure loading the High power batteries at the end of each cycle.

Keywords: Power systems and smart grid, Renewable energy and sustainability, Energy efficient systems
Abstract: The challenging issue of designing an enhanced, stable and robust method capable to track the maximum power point (MPP) of a PV is presented. In the present work, the notion of the virtual optimal resistance (VOR) is firstly introduced while a merit analysis of its importance on the current and voltage characteristic of a PV is indicated. Secondly, a neural network (NN) based implementation is proposed with its swift and robust response to effectively provide a novel and successful methodology of the MPP tracking. The advantage of the method is that it drives the PV power production to its MPP without creating further ripples or oscillations on the output signal. More specifically, we exploit the fact that the MPP is a unique point on the PV current-voltage (I-V) characteristic and that VOR results from the intersection of the I-V curve with the line connecting the origin with the MPP. Then, in order to utilize this new entity on determining the MPP, a reliable and fast, on-line estimation of the VOR is required. This task is realized by the proposed NN scheme. The NN structure and the training procedure are precisely explained in the paper, whereas the very good performance of the methodology is examined. Different simulation studies are conducted and the results are compared with the most popular perturb and observe (P&O) technique to indicate the improvements achieved.

Keywords: Power systems and smart grid, Networked systems
Abstract: In this paper, we analyse the existence and uniqueness of equilibria of constant power loads (CPLs) in meshed DC microgrid architectures. Given the CPLs’ nonlinear characteristic and negative impedance behaviour, they are commonly known to introduce a destabilising effect into the system, effect intuitively coined as negative impedance instability. In the present approach, we start by deriving the characteristic polynomial from the power balance equation aiming to observe the nature of the CPLs voltage solutions, and assess their feasibility. Then, the algebraic expression is transformed into a problem of existence and uniqueness of a fixed point, and further tested by means of contraction mapping theory. A sufficient condition for the sources’ voltage references is obtained to guarantee the existence and uniqueness of equilibria. This provides a useful guidance in selecting the voltage references in the control design process. A numerical investigation on a meshed DC microgrid is carried out to verify the acquired sufficient condition and its underlying developed theory.

Keywords: Power systems and smart grid, Nonlinear systems, Distributed systems
Abstract: This work proposes a distributed robust control architecture for meshed DC Microgrid networks. Each interlinking converter is modelled as a network node and is connected in parallel to a constant power load representing the network’s power consumption. Each node employs a local controller consisting of two parts; current regulation based on a modified version of the state-limiting PI and a distributed MPC driving the system to desired setpoints. We analytically prove each controller’s robustness to model variations caused by changes in both the power demand and the transmitted information among the subsystems. The concept of positive invariance sets and the inherent robustness properties of the nominal MPC are used to prove recursive feasibility of the optimal control problem and guarantee constraint satisfaction at all times. The stability proof of the cascaded node dynamics is based upon the emerging properties of both the state limiting PI and the distributed MPC design. Demonstration of the results is given in a simulated scenario.

Keywords: Power systems and smart grid
Abstract: In this paper a global sensitivity analysis technique for testing impedance measurement algorithms is described. The technique is based on the Analysis of Variance (ANOVA) statistical method. Accuracy of impedance measurement algorithms, when influenced by uncertainties, can be systematically analyzed. This technique divides variance of a measurement algorithm output into components related to uncertain parameters (factors) and interactions between factors. As an application example, we simulate transmission line faults with varying values of fault parameters (factors) according to the Sobol’s quasi-random sampling. The algorithm for automating this task was developed via DIgSILENT Programming Language (DPL). The SIMLAB software is used for generating samples in a factor space according to the Sobol’s quasi-Monte Carlo technique.

Keywords: Power systems and smart grid, System identification, Process control
Abstract: Low-frequency oscillations (LFOs) in the power system have emerged as a significant possible cause of abrupt wide-area blackouts in several parts of the world. As consumer demand grows, so does the insecurity of the operational stability points of power systems as a result of unforeseen events such as connection and disconnection of loads, generators, or inter-area power networks. Although the stochastic subspace identification performed well in identifying certain LFO modes, it gives inaccurate results due to incorrect model order estimates. Incorrect estimation of model order results in the inclusion of trivial modes to essential subspace modes of a power system. The Sequential Cumulative Sum (SCUSUM) approach of order estimation detects changes in the mean with respect to the eigenvalues. This paper proposes the use of the SCUSUM method instead of estimating using the singular value decomposition of the weighted projection matrix in Stochastic Subspace Identification.

Keywords: Intelligent transportation systems, Decentralized control, Automotive control
Abstract: Trajectory planning of connected and automated vehicles (CAVs) poses significant challenges in a mixed traffic environment due to the presence of human-driven vehicles (HDVs). In this paper, we apply a framework that allows coordination of CAVs and HDVs traveling through a traffic corridor consisting of an on-ramp merging, a speed reduction zone, and a roundabout. We study the impact of different penetration rates of CAVs and traffic volumes on the efficiency of the corridor. We provide extensive simulation results and report on the benefits in terms of total travel time and fuel economy.

Keywords: Intelligent transportation systems, Modelling and simulation, Optimisation
Abstract: Throughout the past decades, different versions of the widely used first-order Cell-Transmission Model (CTM) have been proposed for optimal traffic control. Highway traffic management techniques such as Ramp Metering (RM) are typically designed based on an optimization problem with nonlinear constraints originating in the flow-density relation of the Fundamental Diagram (FD). Most of the extended CTM models are based on the trapezoidal approximation of the flow-density relation of the Fundamental Diagram (FD) in an attempt to simplify the optimization problem. However, this relation is naturally nonlinear, and crude approximations can greatly impact the efficiency of the optimization solution. Here, we propose a class of extended CTMs that are based on piecewise affine approximations of the flow-density relation such that (a) the integrated squared error with respect to the true relation is greatly reduced in comparison to the trapezoidal approximation, and (b) the optimization problem remains tractable for real-time application of ramp metering optimal controllers. A two-step identification method is used to approximate the FD with piecewise affine functions resulting in what we refer to as PWA-CTMs. These models are evaluated by the performance of the optimal ramp metering controllers, e.g. using the widely used PI-ALINEA approach, in complex highway traffic networks. Simulation results show that the optimization problems based on the PWA-CTMs require less computation time compared to other CTM extensions while achieving higher accuracy of the flow and density evolution. Hence, the proposed PWA-CTMs constitute one of the best approximation approaches for first-order traffic flow models that can be used in more general and challenging modeling and control applications.

Keywords: Intelligent transportation systems, Linear systems, Decentralized control
Abstract: Lane-free vehicle driving has been recently proposed for connected automated vehicles. Lane-free traffic implies that incremental changes of the road width lead to corresponding incremental changes of the traffic flow capacity. Internal boundary control (IBC) was introduced to flexibly share the total road width and capacity among the two traffic directions of a highway in real-time, so as to maximize the cross-road infrastructure utilization. Centralized solutions, requiring information from the whole highway stretch under consideration, have already been proposed, which, however, may be problematic for long highways with respect to the required communications and physical system architecture in real-time operation. This paper introduces an overlapping decentralized control scheme for IBC of lane-free automated vehicle traffic, based on a contractible controller, which is designed in a decomposed way (per subsystem) for an extended system. Simulation investigations, involving a realistic highway stretch and demand scenario, demonstrate that the proposed decentralized regulator is similarly efficient as the centralized solutions

Keywords: Automotive control, Modelling and simulation, Neural networks
Abstract: In this paper a novel observer design method is proposed, which combines Linear Parameter-Varying-based (LPV) and machine-learning-based design tools. As a first step, a parameter optimization technique is developed to achieve a polytopic LPV formulation of the system model. This modeling technique also involves a machine-learning-based solution to determine scheduling parameters for the LPV system. In the second step, a LPV-based observer design based on the achieved system representation is proposed. Finally, the operation and the effectiveness of the proposed observer algorithm are demonstrated through a vehicle-oriented estimation problem, i.e., estimation of the lateral velocity. In the paper two simulations illustrate the accuracy and the advantageous impact of the observer on the control performances of the closed-loop system.

Keywords: Intelligent transportation systems, Automotive control, Autonomous systems
Abstract: Traffic shock waves are well-known naturally occurring phenomena that lead to unnecessary congestion in highway networks. Introducing connected autonomous vehicles (CAVs) to highways of human-driven vehicles (HDVs) allows for the development of traffic control schemes that can mitigate the effects of the shock waves. In this work, we propose a shock wave detection algorithm based on communication between CAVs with local traffic information. The proposed methodology is suitable for multi-lane mixed traffic highways of arbitrary structure, i.e., it is not limited to closed-circuit ring roads. We show that the detection information can be used to design a class of proactive shock wave mitigating CAV controllers. The choice of the controller can depend on design parameters such as the aggressiveness of the driving behavior allowed. We also demonstrate the importance of the positioning of autonomous agents in multi-lane scenarios. The shock wave dissipation efficiency is evaluated on a three lane circular highway loop using realistic traffic simulation software and low CAV penetration levels.

Keywords: Petri nets, Embedded control systems, Process control
Abstract: In practice, manual planning and implementation of control function logic in building automation and control systems (BACS) is a source of failure. Textual descriptions of control functions are often not standardized and inaccurate. Therefore, they may result in misunderstandings in the following implementation process. The increasing complexity of building energy systems makes the problem even worse. Regarding this problem, we have introduced the MODI method in previous work. The MODI method enables a systematic development of mode-based control algorithms and utilizes signal-interpreted Petri nets as a formalized description method to avoid ambiguity in the description of control algorithms. However, the implementation of the mode-based control algorithms in BACS remains unclear. Manual programming of the algorithms is in practice time-consuming and error-prone. In this paper, we introduce a software-assisted framework to support the implementation of mode-based control algorithms in programmable logic controllers (PLCs) by generating PLC code automatically. In a case study, we apply the framework to implement a mode-based control strategy for an air handling unit and test the strategy in a simulation. In future work, a digital data source and exchange method across the planning and the construction processes of BACS will be considered as a data input of this framework to further facilitate the generation of PLC code.

Keywords: Intelligent transportation systems, Automotive control, Adaptive control
Abstract: The ever-increasing growth of the car industry and the demand for personal vehicles have put current traffic management systems and infrastructure to strain. The enlarged number of vehicles in traffic flows often creates congestion due to the increased demand to use the existing road capacity. This is especially evident in urban areas that consist of urban roads and urban motorways. Increasing the capacity by building additional infrastructure is not always a feasible solution. Thus, approaches derived from Intelligent Transportation Systems are frequently used to increase the level of service, especially on urban motorways. The development of Connected and Autonomous Vehicles (CAVs) creates additional challenges and opportunities for the traffic management system to cope with. In this study, the Variable Speed Limit (VSL) based on Q-Learning (QL) with CAVs as actuators and mobile sensors combined with Speed Transition Matrices (STMs) for state estimation named STM-QL-VSL is developed and analyzed. Varying traffic scenarios with different CAV penetration rates are analyzed, including the comparison of motorway configuration with one and two applicable VSL zones. The developed STM-QL-VSL algorithm managed to learn the control policy for each tested scenario and improve measured macroscopic traffic parameters such as Total Time Spent and Mean Travel Time.

Keywords: Education and training, Mechatronic systems
Abstract: In the current research a holistic tool towards STEM education is introduced. The proposed method is imple- mented using an enhanced version of a previously introduced educational framework [5], namely HYDRA 2.0. The proposed system offers a set of hardware and software tools which allow the creation of working artefacts. An application using aug- mented reality has been developed allowing the users to interact and obtain information about the different building blocks. In order to adopt the aforementioned tools in a classroom environment, an educational procedure consisting of sequential tasks and actions has been introduced and modeled using Timed Petri nets, for representation, monitoring, what-if analysis and root cause analysis purposes. To highlight the applicability of the proposed approach a sample test case which results in a mobile robot with autonomous capabilities is presented and future extensions and conclusions are discussed.

Keywords: Computational methods, Modelling and simulation
Abstract: It is crucial to choose the appropriate numerical method for treating partial differential equations in shape optimization and control problems. This paper introduces a meshless approach derived from the well-known charge simulation method. Instead of a large number of heuristically located monopoles (i.e. charges or sources), the proposed technique relies on more rigorously located poles with multiplicity. A well-conditioned method is devised by applying basis orthogonalization in this multipole expansion. The basis size is determined by a recursive process of orthogonalization in order to achieve the desired accuracy as shown in the numerical examples.

Keywords: Biomedical engineering, Signal processing
Abstract: In this study, we propose a novel family of onset and offset detection algorithms for electromyographic (EMG) signals, based on the Teager-Kaiser Energy Operator (TKEO). These algorithms are derived from an existing double-threshold statistical detector, which is modified to use Shifted Skew Log Laplace Distribution (SSLLD) probabilities and likelihoods to take advantage of the improved TKEO SNR ratio. The performance of the proposed algorithms are compared against existing approaches on synthetic EMG signals generated using an heteroscedastic autoregressive Gaussian model.