The rapid proliferation of Internet of Vehicles (IoV) technology has significantly enhanced traffic efficiency and driving safety, yet it has also introduced severe security challenges due to malware and cyberattacks. This paper proposes a novel Fractional-Order Attack-Defense Game model (FADG-IoV) to address dynamic malware propagation in IoV environments. By integrating fractional-order dynamics, the model accounts for communication delays, traffic density heterogeneity, and channel fading, capturing memory-dependent behaviors inherent in IoV systems. We introduce the Fractional-Order Attack-Defense Game Sparse Attention Multi-Agent Soft Actor-Critic (FADG-SMASAC) algorithm, a model-free reinforcement learning approach that leverages sparse attention mechanisms to achieve adaptive and robust control without requiring a known system model. Through multi-baseline experiments, we validate the FADG-IoV model and FADG-SMASAC algorithm, demonstrating superior convergence, scalability, and robustness compared to existing methods. Our findings highlight the effectiveness of fractional-order game-theoretic strategies in enhancing IoV security against dynamic malware threats, paving the way for future research in adaptive defense mechanisms.

In this article, the constrained navigation problem for autonomous robots moving in unknown cluttered environments is considered. In particular, it is required to ensure the safety of the path planning and control units during the on-line operations. This statement gives rise to a networked control framework whose the key critical aspects are addressed by resorting to model predictive control technicalities developed within a set-theoretic approach. In particular, a novel control architecture is conceived whose the main features can be summarized as follows: anti-collision capabilities despite time-induced time-delay occurrences along the communication medium; mission accomplishment despite unpredictable obstacle occurrences along the nominal path. These properties are formally proven together with ultimate uniformly boundedness and constraints fulfillment of the regulated trajectory regardless of the vehicle uncertainties. In particular, skid-steered tracked mobile robot are considered for their flexibility and adaptability to operate in arduous scenarios for hazard missions. Final experiments are provided to show the effectiveness and to highlight the main advantages of the proposed control architecture.

In this paper, the problem of controlling autonomous vehicles in a Flexible Manufacturing System is addressed in order to optimize logistic operations. To this end, vehicles are required to navigate between machines and from/to the Load/Unload station. The core contribution of this paper is to propose a set-theoretic distributed Model Predictive Control in charge of controlling the autonomous vehicles properly integrated with a Reinforcement Learning scheme to address the routing problem. In addition, vehicles are organized as platoons in order to improve the efficiency of the overall architecture. The numerical simulation shows the effectiveness of the proposed approach.

In the context of Industry 5.0, where environments are subject to unpredictable changes, achieving real-time adaptability and robust collision avoidance is essential for safe navigation and timely task execution. This paper introduces a Robust Grid-Based Receding Horizon Control scheme tailored for multi-robot logistics, designed to address dynamic obstacles and bounded multiplicative disturbances in unicycle-type robots. The method employs a single grid graph, continuously updated in real time using LiDAR data. A distributed robust set-theoretic model predictive control strategy leverages the grid graph to ensure safe and efficient navigation. The proposed approach is validated through realistic simulations in ROS/Gazebo, demonstrating its effectiveness in complex and dynamic scenarios.

In this paper, autonomous vehicles are considered for addressing logistic operations in manufacturing systems. The starting idea consists in organizing a given group of autonomous robots/vehicles in a finite set of platoons in charge to accomplish prescribed job(s) within the manufacturing system. Three aspects are then needed to be formally outlined: task scheduling, routing decisions and command inputs computations. Here, a new distributed multi-layer architecture has been conceived by using three methodologies: timed colored Petri nets, deep reinforcement learning and model predictive control. Roughly speaking, timed colored Petri nets are exploited to formally model the manufacturing system so that an optimal scheduling task complying with the required jobs and the available vehicles is derived; then, run-time routing decisions are obtained by using a distributed reinforcement learning algorithm which exploits the available information provided by the vehicle sensor module; finally, the distributed model predictive control algorithm is built by resorting to a set-theoretic approach where most of the computations are off-line performed. A flexible manufacturing system consisting of four machines and a Load/Unload station is used for simulation purposes. Specifically, five jobs are hypothesized and some scenarios with an increasing number of available vehicles are simulated. In order to evaluate the benefits of the proposed approach, a time criterion based on the completion of all the jobs is considered with the aim to put in light that increasing the number of vehicles improves the control performance until congestion phenomena become unavoidable. Note to Practitioners—This paper proposes an innovative methodology for addressing the logistic operations within flexible manufacturing systems (FMSs) by using a team of autonomous ground vehicles. Unlike existing approaches, the core of this framework consists in combining along a hierarchical structure the capabilities of timed colored Petri nets and the deep reinforcement learning techniques to determine a near-optimal scheduling and run-time routing decisions that are provided to the distributed model predictive units in charge to accomplish the prescribed task. This multi-layer architecture has two main merits: a single platoon, completely disconnected from the others, is devoted to perform its own job; computational burdens are affordable during the on-line operations because most of the computations are moved in the off-line phase.

In this article, we address a constrained regulation problem for networked control systems where the plants are modeled by polytopic linear descriptions, the state vector is partially available via output measurements, and the communication medium is unreliable. A control architecture is then proposed by considering a state-estimation-based robust model predictive control (MPC) strategy, designed to be resilient to regulation challenges while also preventing communication breakdowns when the convergence to the target is not practicable. Specifically, a deconvolution state observer is used for reconstruction purposes, and it is integrated with set-theoretic receding horizon principles to conceive a framework that meets both resilience and communication maintenance requirements.

This paper delves into the resilience challenges encountered by platoons of self-governing agents navigating city streets, particularly focusing on the impact of misleading data infiltrating neighborly communication channels. The core issue centers around defining the trajectory paths of vehicles through routing decisions that must adhere to traffic fl ow constraints. To address this, we employ a decentralized model predictive control (MPC) strategy, leveraging deep reinforcement learning (DRL) abilities for effective decision-making in managing the regulation tasks. Additionally, the paper explores cybersecurity concerns by developing an effective anomaly detection system and practical attack countermeasures to enhance the resilience of the vehicle platoons against data tampering and other cyber threats. Through simulations and experiments, we demonstrate the efficacy of our approach in maintaining smooth traffic flow and ensuring secure communication within autonomous vehicle networks.

This paper addresses the problem of motion planning for an autonomous, tracked mobile robot whose mathematical model depends on uncertain parameters, with constrained moving capabilities, operating in a cluttered environment. The proposed algorithm exploits the concept of one-step ahead controllable sets. In particular, motion sequences compatible with uncertainties and nonlinear model dynamics are determined in the off-line phase with the aim to determine a collision-free path capable to accomplish the given mission. Conversely, the on-line operations are devoted to determine the most appropriate control action by solving a computationally simple optimization problem. Finally, some preliminary numerical results are instrumental to testify the effectiveness of the proposed approach.

In this paper a novel Grid-based Receding Horizon Control strategy for fleet of unicycle robots within the dynamic context of Industry 5.0 environments is developed. The control architecture leverages grid-based mapping for path planning while incorporating a receding horizon control mechanism to enhance navigational efficiency and adaptability in environments characterized by uncertainty and dynamic obstacles. Specifically, the approach addresses the challenge of non-holonomic constraints in unicycle robots by resorting on feedback-linearization method and ensures safe and real-time navigation by means of set-theoretic arguments. Simulation results validate the effectiveness of the proposed controller in maintaining path fidelity, avoiding obstacles and inter-robot collisions dynamically.

In this paper, a distributed control architecture is presented for addressing the target capturing problem of a multi-agent system whose dynamics is described by double integrator models subject to bounded disturbance effects. Starting from novel kinematic models used as reference trajectories, the aim consists in driving the multi-agent system within the containment region without entering the distancing one and, whenever necessary, there remaining confined. The underlying control problem has been tackled by means of model predictive control arguments. In particular, two different distributed strategies have been developed, and adequately switched to each other, in order to guarantee constraint satisfaction within the capturing region despite any disturbance realization. Note to Practitioners—This paper proposes a methodological solution for dealing with the capturing problem for multi-agent systems operating in uncertain environments where the target is defined by the ring resulting from the distancing and containment ellipsoids. Differently from the existing literature, the proposed approach combines into a unique framework properties coming from potential fields theory and distributed model predictive control philosophy. It is interesting to put in light that the underlying control strategy allows the users to face surveillance and rescue operations, to cite a few, in computational affordable way due to the required memory resources because most of computations can be straightforwardly moved in the off-line phase.

In this article, a novel approach to generating kinematic state trajectories for single-integrator multiagent systems is developed with the aim of addressing cooperative surveillance tasks of rectangular areas. In particular, the key idea consists in driving the involved agents within a so-called containment region while simultaneously reaming outside a forbidden area around the prescribed target. As one of its main features, the proposed kinematics allows the whole multiagent configuration to rotate safely along the perimeter under both full and partial connectivity properties of the underlying topology graph. Steady-state conditions are analyzed and sufficient conditions are derived in terms of kinematic model parameters. Finally, a set of simulations is aimed at showing the capability of the kinematic descriptions to quickly buttonhole the containment region and to keep a rotating behavior around the target.

In this article, the problem of generating multimodel state space descriptions in a data-driven context to embed the dynamic behavior of nonlinear systems is addressed. The proposed methodology takes advantage of three ingredients: 1) linear time-invariant system behavior; 2) data-driven modeling; and 3) reinforcement learning (RL) technicalities. These elements are properly combined to develop a data-driven algorithm capable to derive an accurate outer convex approximation of the nonlinear evolution. In particular, an actor-critic RL scheme is designed to efficiently comply with the exhaustive research on the whole parameter space. At each iteration, the effectiveness of the obtained uncertain polytopic model is tested by a probabilistic approach based on a confidence level metrics. As the main merits of the proposed approach are concerned, the following aspect clearly stands up: the development of an interdisciplinary methodology that takes advantage of system theory, probabilistic arguments and RL capabilities giving rise to an harmonized architecture in charge to deal with a vast class of nonlinear systems. Finally, the validity of the proposed approach is tested by resorting to benchmark examples that allow to quantify the level of accuracy of the computed convex hull.

In this paper, the resilient control problem for constrained cyber-physical systems subject to stealthy data intrusions on the communication channels is considered. The key idea consists in designing a neural network to act as the anomaly detector during the on-line operations. Accordingly the controller unit, based on model predictive control arguments, is developed to take advantage of the resulting detection capabilities. As its main merits are concerned, the overall control architecture has a two-fold merit with respect to the existing literature: it is avoided the need of modifying the detector structure whenever a different class of attacks is considered, and the occurrence of false positive events is significantly mitigated. Finally, a numerical example is provided to show the effectiveness and peculiarities of the proposed approach.

This paper presents a set-theoretic receding horizon control strategy for platoons of autonomous vehicles driving in smart cities context. In order to reduce traffic and$CO_{2}$emissions, we propose a path planer based on Deep Reinforcement Learning (DRL). The advantages of this solution is the ability to deal with the actual traffic congestion, while driving the autonomous vehicles to their destination and fulfilling the constraints. In particular, the high-level routing decisions are translated into set-points for the receding horizon controllers, making the control actions on the vehicle dynamics more computational efficient. In order to show the effectiveness of the overall architecture, a campaign of simulations on a platoon of eight vehicles, moving in the city center of Bologna in Italy, is provided.

In this paper, a sustainable routing algorithm for vehicle platoons operating in smart urban networks is presented. The proposed approach makes use of deep reinforcement learning (DRL) and set-theoretic model predictive control (MPC). In particular, the learning process aims at reducing traffic congestion and$CO_{2}$emissions, whereas the MPC unit allows to adequately track the assigned path by using real-time traffic data. To adequately analyze the performance of the resulting control architecture, the SUMO and MATLAB environments are used to implement complex operating scenarios where road maps data and vehicle state trajectories can be shared and exchanged. Finally, numerical studies are provided by resorting to the SUMO environment and considering a platoon of five vehicles. The resulting simulation campaign puts in light the capability of the training process to significantly mitigate the$CO_{2}$emissions of the whole platoon: from a minimum of 3.7 % to a maximum of 13% with respect to the use of the well-known Dijkstra algorithm.

In this paper, the path planning problem under routing decisions is considered for platoons of autonomous vehicles moving in urban road networks. The key idea is to exploit and adequately combine arguments coming from two research fields: deep reinforcement learning and model predictive control. Along these lines, a novel control architecture is proposed and its feasibility formally proved. In particular, the high-level routing decisions arising from the distributed deep reinforcement learning operations are translated into manipulable set-points for the underlying bank of receding horizon controllers by making more computational affordable and efficient the action on the vehicle dynamics. Finally, some simulations on platoon, consisting of three agents described by double-integrator models, are provided to show the effectiveness of the overall architecture.

In this article, a novel distributed model predictive control architecture is proposed for the coordination and control of multivehicle formations moving within uncertain environments. As one of its main merits, multiagent swarm modeling and leader-follower configurations are jointly exploited within an ad hoc model predictive control framework to reduce as much as possible the use of onboard sensors that is essential in long-range missions. Moreover, the feasibility and asymptotic closed-loop stability of the resulting scheme are formally proved. Finally, a laboratory experiment is used to show the effectiveness of the proposed algorithm with particular attention to the follower capability of operating in a so-called blind fashion.

In this paper, an adaptive sensor selection architecture is developed to deal with distributed state estimation problems for multi-agent networked systems consisting of three different classes of nodes (plants, sensors and agents). Specifically, the problem of adequately fusing the sensors data coming from the plants and delivered to the agents, is addressed by evaluating their trustworthiness. This is achieved by exploiting a well-established approach in the power electronics: the Perturb&Observe algorithm that in the present framework allows one to select the more adequate group of sensors so as to compute at each time instant the best state estimate according to a given performance index. Some simulations are finally reported to testify the effectiveness of the proposed methodology.

In this paper, we present a novel resilient control architecture capable to manage replay attacks for multi-agent discrete-time linear systems subject to input and state constraints. By considering a leader-follower configuration, the basic idea consists of exploiting model predictive control arguments to apply adequate control action in order to isolate the attacked unit that otherwise could compromise system operations. To this end, a set-theoretic receding horizon control strategy is developed that is also capable to instantaneously detect the attacked agent along the platoon chain. Finally, we describe a set of simulations on a group of mobile robots to demonstrate the effectiveness of the proposed approach.

In this paper, a target capturing problem for multi-agent systems described by double-integrator dynamics and time-varying communication network has been considered.The main aim consists in ensuring that the agents are indefinitely confined into a so-called containment region by jointly lying outside the distancing set and keeping this property for all future time instants. Here, this problem has been addressed by means of a distributed control architecture based on two ingredients: a new description of the kinematic evolution of the multi-agent system and an adequate customization of model predictive control ideas in a distributed fashion. As one of its main merits, the resulting scheme ensures synchronization capabilities in finite time for all the involved agents.

In this paper, a model-based maintenance approach is developed for rolling stocks vehicles operating along railway networks. By considering the high management costs in the modern and complex railways fleets as a primary requirement, the key goal of the proposed approach consists in efficiently integrating maintenance actions with the capability to satisfactorily keep railway services. Here, this is achieved by means of multi-layer approach that combine into a single framework the following ingredients: interpolation procedures, machine learning algorithms and prediction arguments that take advantage of an accurate model description of the rolling stock dynamics. Experiments on a PV7 EVO - Matisa, owned by the Italian Railways Network, have been conducted with the aim to show the effectiveness of the proposed maintenance architecture.

In this paper, a novel control architecture capable of managing denial-of-service attacks affecting the communication links between a group of interconnected systems and remote controllers is presented. The basic idea relies on the representation of the interconnected cyber-physical system as a leader-follower configuration so that adequate control actions are computed in order to isolate the attacked unit that otherwise could compromise system operations. Simulations on a multiarea power system confirm that the proposed control scheme can reconfigure the leader-follower structure in response to denial-of-service (DoS) attacks occurring on both sensor-to-controller and controller-to-actuator channels.

In this paper, a distributed receding horizon control scheme is developed for teams of autonomous agents customized as swarms within platoon configurations. Coordination and collision avoidance specs for multi-agent systems prescribe the use of high memory requirements for local computations and the exploitation of a growing number of sensors as the involved agents increase. The natural consequence is that usually short-range missions are allowed. In order to mitigate such a drawback, two key ingredients are here exploited: 1) the swarm formation modelling that allows to consider in some sense (it will be later clarified) several agents acting as a singleton; 2) an ad hoc model predictive scheme capable to adequately exploit swarm kinematics properties to ameliorate energy consumption savings.

In this paper we present a distributed supervisory strategy for load/frequency control problems in networked multi-area power systems. Coordination between the control center and the areas is accomplished via data networks subject to communication latency which is modelled by time-varying time-delay. The aim here is at finding strategies able of reconfiguring, whenever necessary in response to unexpected load changes and/or faults, the nominal set-points on frequency and generated power of each area so that viable evolutions arise for the overall networked system and a new suitable equilibrium is reached.

In this paper we present a supervisory strategy for load/frequency control problems in networked multi-area power systems. The scheme consists of modifying the nominal frequency set-points and adjusting offsets on the nominal Automatic Load-Frequency Control (ALFC) laws of each area in order to avoid operative constraints violations in response to unexpected load changes and/or faults/failures. This aim is accomplished via a recent predictive control approach named Parameter Governor, which joins in a general framework Reference and Offset Governors actions. The effectiveness of the strategy is demonstrated on a two-area power system subject to coordination constraints on maximum frequency deviations and generated and distributed inter-area powers.