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 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 paper aims to investigate the challenging problem of a multi-agent game with multiple pursuers and a single evader in an environment with multiple unknown uncertainties. A coupled approach combining decentralized observers and reinforcement learning (RL) controllers is proposed to deal with this scenario. Firstly, decentralized observers driven by auxiliary control laws are introduced to estimate the states of uncertain systems, with their best responses obtained through the adaptive dynamic programming (ADP) method. The estimated states, which reflect the actual states of the pursuers’ systems, are concurrently transmitted to the RL controllers. Subsequently, the controllers are trained with observer-based heterogeneous-agent proximal policy optimization (OHAPPO) algorithm, in which a novel global multi-function cost is designed. The algorithm utilizes the advantage decomposition for policy updates in the way of credit assignment, resulting in more stable and efficient updates compared to traditional value decomposition. Moreover, to further enhance the performance of both observers and controllers, a sequential game is established between them, where observers’ policies are influenced by controllers’ optimal control and vice versa. Finally, the simulation results verify the effectiveness of the designed OHAPPO algorithm in the pursuit-evasion game.

This paper explores the consensus control problem of nonlinear multi-agent systems (MASs) under complex cyber-physical threats (CPTs), which encompass sensor/actuator faults, input/output channel noises, and random cyber-attacks. The multiple sensor/actuator faults are uniformly modeled as an exponential type, while random cyber-attacks are characterized by a Markov chain. To enhance the safety and security of MASs under CPTs, the distributed normalized observers are first developed, enabling precise estimations of unknown state and fault information. Subsequently, the distributed fault-tolerant consensus control (FTCC) scheme with a positive reconstruction mechanism is proposed to maintain resilience against attacks, compensation for faults, and robustness to noises in MASs under adverse CPTs. The two notable innovations can be outlined as follows: i) The achievement of FTCC objectives under complex CPTs, demonstrating strong algorithmic transferability in both non-attack and random attack scenarios. ii) The adoption of a double-layer distributed framework in the estimation layer and control layer, balancing computational complexity and efficiency improvements compared to a combination of decentralized and distributed approaches. Simulation results finally confirm the efficacy and feasibility of the proposed FTCC 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.

In this study, the fault-tolerant consensus control (FTCC) challenge is investigated for nonlinear multiagent systems (MASs) in the simultaneous occurrence of abrupt and incipient actuator/sensor faults in the physical level and hybrid Deception/Denial-of-Service (DoS) attacks in the cyber level. For security enhancement and/or safety maintenance purposes, an unknown state and fault decoupling-based augmented estimator is first devised, and a distributed event-triggered FTCC protocol is then developed to achieve strength against hostile attacks and faults, respectively, with the incorporation of augmented state estimation, neighboring sensor fault estimation, and latest successfully triggered output interaction. By constructing dual indicators along with average dwelling time and attack frequency technique, criteria of exponential mean-square consensus of the nonlinear MASs subject to hybrid attacks are obtained. In the end, simulation is outlined to illustrate the efficacy and improvements of the developed event-triggered FTCC methodology.

To achieve fault diagnosis and prognosis, obtaining adequate and reliable life-cycle data is essential. However, this poses a challenge in current high-reliable Internet of Things (IoT) systems. Fortunately, accelerated degradation testing (ADT) can be employed to overcome this hurdle. Nevertheless, a dependable testing and measuring technique is required to construct an accurate model for ADT. This testing method plays a vital role in evaluating fault diagnosis, prognosis, lifetime, and maintenance decisions for reliable products under operational stress. To ensure effective testing, it is crucial to utilize appropriate models that account for the individual heterogeneity of products. However, the commonly used single stochastic models in ADT overlook the impact of this condition in real-world applications, resulting in misspecification problem. To address this limitation, we propose a novel mixed stochastic process model that integrates multi-Wiener processes and dynamic weights. In addition, we leverage interval analysis to analyze system lifetime, considering the limited data size. The estimation of unknown parameters in our mixed model is achieved using the Metropolis–Hastings algorithm. By analyzing stress relaxation data from electrical connectors, we demonstrate the superior accuracy of our mixed model over conventional single stochastic models in ADT.

This study explores the leader-following consensus tracking control issue of multiple unmanned surface vehicles (multi-USVs) in the presence of malicious connectivity-mixed attacks in the cyber layer, and concurrent output channel noises, sensor/actuator faults, and wave-induced disturbances in the physical layer. Sensor/actuator faults are initially modeled with unified incipient and abrupt features. Additionally, connectivity-mixed attacks are depicted using connectivity-paralyzed and connectivity-maintained topologies through nonoverlapping and switching iterations. The standardization and observer design in multi-USVs are incorporated to decouple the augmented dynamics and estimate unknown state, fault, and noise observations, and then a defense and fault-tolerant consensus tracking control approach is designed to accomplish the robustness to disturbances/noises, resilience to attacks, and tolerance to faults, simultaneously. The criteria for achieving leader-following exponential consensus tracking of multi-USVs with cyber-physical threats can be determined based on activation rate and attack frequency indicators. Comparative simulations outline the effectiveness and economy of the proposed defense and tolerance technique against sensor/actuator faults and cyber-attacks on multi-USVs.

This study investigates the distributed secure control problem of multiple unmanned surface vehicles (USVs) in the presence of wave-induced disturbances and unified abrupt and incipient rudder angle faults in physical layer, and aperiodic Denial-of-Service (DoS) attacks in cyber layer. Multi-USVs with rudder angle fault and DoS attack modeling are first established. Then, the decentralized unknown input observer (UIO)-based fault estimation and distributed secure control approach is developed in a co-designed framework for multi-USVs with cyber–physical threats. Advantages of the proposed secure scheme are: 1) actuator faults, DoS attacks, and event-triggering strategies with varying action instants, durations, and locations are synchronously addressed and 2) the criteria of exponential consensus are derived by virtue of attack frequency and average dwelling time technique without prior knowledge of unknown wave-induced perturbation bounds and elimination of Zeno behavior in an event-based mechanism. Comparative simulations outline the performance and advantage of the proposed distributed secure control algorithm.

Vehicle platoon systems are considered as automatous vehicles in a platoon-based driving pattern in which a following vehicle follows the preceding vehicle and maintains the desired vehicle spacing. This article investigates the leader-following tracking issue of vehicle platoon systems under cyber-physical threats with the distributed antiattack fault-tolerant tracking control strategy. In this study, vehicle platoon systems, complicated actuator faults in physical layer, and connectivity-mixed attacks in the cyber layer are modeled, respectively. Decentralized fault-estimation unknown input observer and distributed antiattack fault-tolerant tracking control designs are developed in an integrated control framework to guarantee the robust and resilient tracking property of estimation errors and platoon tracking errors as well as the reliable intervehicle spacing by virtue of attack activation rate and attack frequency metrics. Simulations validate the proposed distributed antiattack fault-tolerant tracking control algorithm in pernicious cyber-physical threatened scenarios.

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

This paper proposes a novel distributed fault-tolerant consensus tracking control design for multi-agent systems with abrupt and incipient actuator faults under fixed and switching topologies. The fault and state information of each individual agent is estimated by merging unknown input observer in the decentralized fault estimation hierarchy. Then, two kinds of distributed fault-tolerant consensus tracking control schemes with average dwelling time technique are developed to guarantee the mean-square exponential consensus convergence of multi-agent systems, respectively, on the basis of the relative neighboring output information as well as the estimated information in fault estimation. Simulation results demonstrate the effectiveness of the proposed fault-tolerant consensus tracking control algorithm.

This paper proposes a novel decentralized output sliding-mode fault-tolerant control (FTC) design for heterogeneous multiagent systems (MASs) with matched disturbances, unmatched nonlinear interactions, and actuator faults. The respective iteration and iteration-free algorithms in the sliding-mode FTC scheme are designed with adaptive upper bounding laws to automatically compensate the matched and unmatched components. Then, a continuous fault-tolerant protocol in the observer-based integral sliding-mode design is developed to guarantee the asymptotic stability of MASs and the ultimate boundedness of the estimation errors. Simulation results validate the efficiency of the proposed FTC algorithm.

This paper investigates the attitude and position tracking control problem of the Lead–Wing close formation system with unknown multiplicative actuator faults. In close formation flight, the movement of the Wing unmanned aerial vehicle (UAV) is influenced by the vortex effects of the neighboring Lead UAV. This situation requires a modeling of the aerodynamic coupling vortex effects and linearization on the basis of optimal close formation geometry. Adaptive actuator fault parameters are identified by merging the improved unknown input observers, and adaptive laws with projection functions are presented for actuator fault tracking. The sufficient condition corresponding to the existence of unknown input observers is given. Then, an adaptive fault-tolerant H-infinity output feedback control scheme is developed to guarantee the asymptotic stability, H-infinity closed-loop system performance, and attitude and position tracking properties while the Lead UAV is being maneuvered. Simulation results of the Lead–Wing close formation flight validate the efficiency of the proposed fault-tolerant control algorithm.