Intelligent and connected vehicles (ICVs) under complex environments unavoidably encounter unknown actuator faults and external disturbances, posing threats to the function safety of ICVs. Furthermore, the existence of data packet loss leads to random switching of communication topology, compromising communication security, thereby forming complex cyber-physical threats. To tackle these challenges, this paper devises a double-layer adaptive strategy for an active fault-tolerant platooning control (A-FTPC) framework, aiming to simultaneously achieve both function safety and communication security of ICVs. Here, the term A-FTPC specifically refers to an observer-based active fault-tolerant compensation framework, in which online state and actuator fault estimation is used to support control adjustment, rather than a complete fault diagnosis and isolation architecture in the classical sense. In the upper cyber layer, a distributed adaptive observer based on neighborhood output estimation error, which has dynamic and real-time adaptability to random switching topologies, is designed to stably estimate the self-state and fault information of each following vehicle. Subsequently, an adaptive A-FTPC scheme based on the decoupled actuator fault estimation information and the state information of the leading vehicle is proposed in the lower physical layer. This scheme possesses active compensation for unknown abrupt-incipient actuator faults and robustness against external disturbances, guaranteeing that each following vehicles can track the leading vehicle. Finally, the simulation results confirm the competitiveness and efficacy of the proposed A-FTPC strategy of ICVs.

This article investigates the mean-square output consensus problem for heterogeneous linear multiagent systems (MASs) over random packet loss channels. Agent heterogeneity is reflected in possibly different state dimensions and dynamic parameters. In addition to heterogeneity, a major challenge arises from relaxing the commonly adopted independent and identically distributed (i.i.d.) assumption on packet losses. To capture temporal correlations that are prevalent in practice, packet losses are modeled by a discrete-time Markov process. Since existing consensus controllers designed for i.i.d. losses may fail under Markovian packet losses, novel dedicated control schemes are developed. Two packet loss scenarios are considered: identical and nonidentical packet losses. For identical packet losses, where all channels drop packets simultaneously, both analytical and numerical consensus conditions are derived to guarantee consensus of the distributed observers. The analytical condition reveals the interplay among packet loss rate, communication topology, and system dynamics, while the numerical conditions are more computationally tractable. An output-regulation-based controller is then designed to achieve mean-square output consensus. For the more general case of nonidentical packet losses, edge Laplacian theory is employed to decouple packet loss processes from the communication topology, leading to consensus conditions for the distributed observers, as well as corresponding controllers that guarantee mean-square output consensus. Finally, numerical simulations are utilized to validate the results.

This article addresses the formation control problem of underactuated multi-autonomous surface vehicles (ASVs) under denial-of-service (DoS) attacks on the communication network and complex uncertainties, including unknown ASV dynamics, external disturbances, and obstacles. First, a novel distributed target estimator (DTE) using a first-order low-pass filter is designed to estimate the state of the target based on partial observability under target information constraints and DoS attacks. Second, a safe guidance law for the ASVs is developed using the estimated target state and a control barrier function. Third, a “dual-adaptation control and learning” bidirectional fusion model is constructed. Specifically, on one hand, a distributed adaptive formation controller based on value decomposition reinforcement learning (VDRL) is designed. By decomposing the global value function, this controller addresses the credit allocation issue in cooperative formation and allows the ASVs to adjust their strategies autonomously based on the environment. On the other hand, an adaptive mechanism is used to improve the computation strategy of the global value function in VDRL, enhancing the learning efficiency and stability of the reinforcement learning (RL) algorithm in complex environments. The proposed VDRL-based formation control algorithm of the underactuated multi-ASVs ensures accurate target state estimation and convergence of formation errors under DoS conditions. A rigorous theoretical analysis is further used to ensure the closed-loop stability of the multi-ASV systems. Finally, simulation results validate the effectiveness of the proposed distributed formation control algorithm.

This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.

Deep neural networks (DNNs) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this brief, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stability for any compatible neural network parameters. In addition, improved control performance can be achieved by further optimizing neural network parameters. Besides, we provide explicit upper bounds on tracking errors in terms of controller parameters, which allows us to achieve the desired tracking performance by properly selecting the controller parameters. Furthermore, when system models are unknown, we propose an improved Lagrangian neural network (LNN) structure to learn the system dynamics and design the controller. We show that in the presence of model approximation errors and external disturbances, the closed-loop stability and tracking control performance can still be guaranteed. The effectiveness of the proposed approach is demonstrated through simulations.

In the past few years, energy management strategies based on multi-agent reinforcement learning (MARL) have been an active research topic. However, existing MARL algorithms require a massive amount of data, making it challenging to ensure data privacy. To address the problem, this paper investigates FL-MARL that combines federated learning (FL) with MARL. FL allows each agent to train based on local data and only share model parameters, which means that the actual data are not shared among agents. The framework is in conjunction with a MARL algorithm: independent proximal policy optimization (IPPO). The proposed algorithm has two advantages: 1) It can protect data privacy of each agent and 2) It can adapt to large-scale and decentralized data scenarios. Finally, the performance of the algorithm is verified by simulation.

This paper considers the distributed fault-tolerant consensus for heterogeneous nonlinear multiagent systems (HNMASs) with actuator faults and full-state constraints via adaptive dynamic programming (ADP). In order to handle the state constraint problem with multiple constraint types, a unified universal barrier function is introduced to convert the original constrained system into a non-constrained system. For the purpose of improving control efficiency and guaranteeing system reliability, a novel value function including fault estimations and control inputs is established. Considering the limitation of the computation and communication resources, a simplified ADP method incorporating the dynamic event-triggered strategy is developed to learn a distributed event-triggered fault-tolerant control policy. It is strictly proven that the HNMASs’ stability and the neural network weights’ convergence are guaranteed by the Lyapunov theory in the sense of uniform ultimate boundedness. Simulations are presented to verify the proposed control policy.

This article investigates adaptive output formation tracking control of nonlinear multiagent systems with time-varying actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies. Considering the dynamic changes of communication connections in uncertain environments, a double semi-Markov process is first introduced into the leader-follower structure to describe the random switching of communication topologies. Then, a novel adaptive distributed fault-tolerant output formation tracking control framework is established using the backstepping and Nussbaum gain technique to address matched/mismatched uncertainties and disturbances, time-varying actuator faults, and unknown nonidentical control directions. In this control framework, the independent variable of the Nussbaum function is designed as a non-negative function that monotonically increases with respect to time, thereby overcoming the presence of the absolute value of its derivative in the integration process. Based on the distributed structure, an adaptive fault-tolerant controller is further proposed to achieve the asymptotic output formation tracking in mean-square sense. The stability of the closed-loop nonlinear multiagent systems is analysed through the contradiction argument and Lyapunov theorem. The simulation example verifies the effectiveness of the proposed control strategy.