A novel thruster fault detection (FD) strategy is investigated in this article for unmanned marine vehicles (UMVs) under external disturbances and aperiodic denial-of-service (DoS) attacks. To address the challenge of the inability to timely detect DoS attacks, the UMV under consideration and its corresponding filters are initially modeled under the framework of an asynchronous switched system. Then, sufficient conditions guaranteeing the system to be exponentially stable and with the prescribed performances are derived by leveraging the model-dependent average dwell time (MDADT) and piecewise Lyapunov functions (PLFs). Simultaneously, the lower bound of the tolerable sleep interval and the upper bound of the DoS attack interval are rigorously calculated. The design criteria of the FD filters are subsequently obtained through the employment of some decoupling techniques. Finally, the simulations on a UMV demonstrate the effectiveness of the proposed methods.

Frequency stability is vital for the reliable operation of critical equipment in power systems. However, increasing renewable integration and unpredictable actuator faults hinder traditional controllers from achieving frequency regulation within a fixed and predetermined time. To overcome such a challenge, a novel resilient decentralized fixed-time dynamic feedback controller is proposed for multi-area power systems subject to non-homogeneous Markovian jumps, actuator faults, and load fluctuations. In contrast to existing fixed-time control approaches dependent on state change rates, the proposed dynamic controller is uniquely governed by both control amplitude bounds and quadratic state terms. The designed controller rigorously guarantees both the stochastic fixed-time stability and the L∞ performance despite simultaneous actuator failures and non-homogeneous parameter jumps. By applying Lyapunov differential inequalities and set measure theory, a tractable design criterion is established to facilitate the solution of desired gain matrices, ensuring scalability and plug-and-play functionality. A three-area power system is finally utilized to evaluate the effectiveness of the developed control strategy regarding fixed time convergence and resilience against load fluctuations and actuator faults.

The interval observer, recognized as a potent tool for fault detection (FD), diverges from the adoption of fixed thresholds to enhance the timeliness and precision of detection. This study endeavors to design an FD mechanism for fuzzy cyber-physical systems (CPSs) subjected to adversarial influences based on improved attack tolerant interval observer. Specifically, the proposed frequency-information-based unknown input interval observer (UIIO) not only enables a more precise estimation of the interval range under the influence of attack signals but also isolates unknown decoupled inputs. Furthermore, it facilitates specific performance design for signals within a particular frequency range. Following the stability analysis of the system, linear solvable conditions are presented to ensure the robustness, fault sensitivity of the augmented system, as well as non-negativity of the augment system matrix. Finally, the proposed attack-tolerant FD mechanism is validated through simulation examples, including the influence of the prior attack information to detection interval width and the detection performance for small-amplitude faults or faults with specific frequency characteristics.Note to Practitioners—CPSs constitute the core of the next-generation manufacturing industry, applicable in areas like unmanned autonomous systems, smart grids, and intelligent healthcare, capitalizing on wide-ranging cyber space utilization. Nonetheless, this breadth also exposes them to elevated risks, including the threat of actuator attacks. The novel fault detection scheme developed in this paper is capable of operating in real-time when the system is confronted with various threats such as disturbances, faults, and actuator attacks. This approach obviates the requirement for knowledge about the nature of actuator attack signals, while enabling the generation of a system state estimation interval with a tunable error range. Leveraging frequency-dependent techniques, the developed scheme, even in the presence of potential actuator attack and disturbance, is particularly well-suited for the detection of fault signals with specific frequency domain characteristics.

The cooperative control problem for cyber-physical systems under actuator faults and denial-of-service attacks is investigated, where each subsystem can be modeled by an agent and denial-of-service attacks are viewed as attacks against the inter-agent communication. Specifically, a scenario is considered in which the communication topology loses connectivity following a denial-of-service attack. To address the above problem, a fully distributed secure control strategy is proposed. This strategy combines a distributed observer with an observer-based fault-tolerant consensus control method. Therein, the assumptions that the leader’s communication links are unbreakable and the switched topology contains a spanning tree are further relaxed based on the idea of a jointly connected topology. In addition, this paper also has the following features: Firstly, the proposed scheme eliminates the need for all agents to have prior knowledge of the leader’s dynamics, making the scenario considered in this work more general and applicable. Secondly, the control method presented in this paper does not rely on any global topology information, allowing it to be implemented in a completely distributed manner. Building on the proposed scheme, even under denial-of-service attacks and actuator faults, the consensus problem for cyber-physical systems can be realized. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.

This paper mainly focuses on secure fault-tolerant control for nonlinear cyber-physical systems under the influence of multiple threats, i.e., sensor and actuator saturations, actuator fault, and intermittent denial-of-service attacks. A novel secure fault-tolerant control method is proposed within the Takagi-Sugeno fuzzy modeling framework, considering both saturation constraints and intermittent denial-of-service attacks, to ensure the secure performance of cyber-physical systems under multiple threats. By leveraging the advantages of the proposed method, the influence of outdated information caused by denial-of-service attacks is mitigated, and more precise state/fault estimations are obtained. Furthermore, the method guarantees the stability of both the estimation error system and the closed-loop system, even in the presence of intermittent denial-of-service attacks and saturation constraints. Finally, the effectiveness of this method is validated through experiment.

This paper proposes a resilient output-feedback frequency regulation mechanism for multi-area power systems, ensuring secure and stable operation. Firstly, the uncertainties introduced by state-of-charge (SOC) variations are considered for the addressed system involved electric vehicle (EV). Subsequently, the behavior of hybrid replay-DoS attacks is taken into account, where a non-homogeneous Markov jump framework is employed to capture both stochastic transitions and temporal dependencies of attack behaviors, and a decentrilized resilient controller is designed to mitigate these adverse effects. To facilitate controller synthesis, a refined model of the lossless power network is further developed by equivalently transforming tie-line power deviations in specific areas, ensuring compliance with tie-line power constraints while preserving system controllability. Based on this model, a comprehensive analysis of system dynamics is performed, and a set of delay-dependent, computationally tractable criteria is derived for controller gain selection. Finally, extensive simulations on three-area power systems validate the effectiveness of the proposed strategy in maintaining frequency stability, mitigating SOC uncertainties, and counteracting hybrid replay-DoS attacks.

This article investigates the observer-based human-in-the-loop (HiTL) optimal output cluster synchronization control problem for nonlinear multiagent systems (MASs). First, the leader is designed to be nonautonomous, with the unknown time-varying input monitored by the human operator directly. To address the problem that leader's output is not available to each follower, an observer is designed. This observer features practical prescribed-time convergence, and independence of prior knowledge of leader's input. Then, an augmented system consisting of observer dynamics and follower dynamics is constructed and a cost function is formulated. Accordingly, the HiTL optimal output cluster synchronization control problem is transformed into a solution to the Hamilton-Jacobian-Bellman equation (HJBE). Subsequently, the off-policy reinforcement learning algorithm is utilized to learn the solution to HJBE without complete knowledge of the system dynamics. To alleviate computational burden, the single critic neural network (NN) is employed for the algorithm implementation, with the least square method applied for training the NN weights. Finally, the simulation results are presented to verify the validity of the designed control scheme.

This paper investigates the problem of secure control for input saturated nonlinear cyber-physical systems (CPSs) subject to faults and multi-channel non-periodic denial-of-service (DoS) attacks. First, the model is reconstructed where the nonlinear characteristics can be described by using the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, with the help of the sampling mechanism and the anti-windup compensator, a sampled data-based anti-windup fault-tolerant control scheme with membership function mismatch is proposed. This scheme effectively mitigates the impact of faults while alleviating performance degradation caused by input saturation. Furthermore, to address the challenges posed by multi-channel non-periodic DoS attacks, a resilient observer-based anti-windup secure fault-tolerant control strategy is further proposed to guarantee system stability. Finally, the simulation results are also given to verify the validity of the proposed method.

The prescribed-time (PT) human-in-the-loop (HiTL) optimal synchronization control problem for multiagent systems (MASs) under link-based denial-of-service (DoS) attacks is investigated. First, the HiTL framework enables the human operator to govern the MASs by transmitting commands to the leader. The link-based DoS attacks cause communication blockages between agents, resulting in topology switching. Under the switching communication topology, a fully distributed observer is proposed for each follower, which simultaneously integrates a prescribed finite-time function to estimate the leader's output within the PT. This observer is characterized by a bounded gain at the PT point and guarantees global practical PT convergence, while avoiding the use of global topology information. By combining the follower dynamics with the proposed observer, an augmented system is developed. Subsequently, the model-free Q-learning algorithm is used to learn the optimal synchronization policy directly from real system data. To reduce computational burden, the Q-learning algorithm is implemented using a single critic neural network (NN) structure, with the least-squares method applied to train the NN weights. The convergence of the Q-functions generated by the proposed Q-learning algorithm is proven. Finally, simulation results verify the effectiveness of the proposed control scheme.

This article mainly focuses on the problem of observer-based adaptive event-triggered security fault-tolerant control (FTC) for attacked cyber-physical systems (CPSs). First, for the nonlinear characteristics in CPSs, the model is reconstructed based on the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, a T-S fuzzy resilient observer is proposed to estimate the state as well as the actuator fault information. The proposed T-S fuzzy resilient observer proactively detects sensor transmission channels affected by dynamically changing denial-of-service (DoS) attacks, and proactively isolates contaminated data so as to reduce the impact of DoS attacks on the estimation effect. Based on the above content, an observer-based adaptive event-triggered security FTC scheme is proposed, which can ensure the stability of the CPSs and save the limited network resources between the controller and the actuator. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

The model predictive control (MPC)-based asynchronous attack tolerant control scheme is investigated in this article for uncertain Markov jump cyber-physical systems (MJCPSs) under the Denial-of-Service (DoS) attack. To tackle the problem of the system running mode may not be observed in the control center, an asynchronous model predictive controller is proposed. Specifically, a dynamic controller, which can tune the performance online, is designed besides a traditional state feedback one. Even though such a combination may cause possible degradation of system performance, it can expand the initial feasible region and relieve the online computation burden efficiently. In addition, a decision variable is introduced to alleviate limitations on the feasible region generated by the constraints in the traditional MPC method. A series of solvable optimal problems are further constructed to achieve the desired performances. Finally, an application of the proposed method is given to demonstrate its effectiveness.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

In this article, a novel secure fault-tolerant control (FTC) strategy is proposed to deal with the impact of multiple threats such as sparse sensor attacks, system faults, and unknown disturbances on T–S fuzzy cyber-physical systems (CPSs). First, under the assumption of 2s-detectability, a set of robust local unknown input observers is designed. Specifically, each observer can decouple partial disturbances and perform targeted suppression on undecoupling disturbances simultaneously. Next, the residual-based attack detection strategy and secure global estimation fusion mechanism are developed, leading to the estimation of the state and concerned fault with smaller estimation error. Ulteriorly, a secure fault tolerant controller is proposed to ensure that the system can recover satisfactory performance in time subjected to multiple threats. Finally, the proposed secure FTC method is applied to the control scenario of autonomous vehicles in the network environment, which proves the effectiveness of the developed technology.

This paper proposes a novel dynamic eventtriggered scheme for nonlinear multi-agent systems under denialof-service (DoS) attacks via an adaptive fuzzy resilient control method. At the beginning, fuzzy logic systems are utilized to identify the unknown system dynamics. Then, a reliable attack detection mechanism forms the basis for establishing a dynamic event-triggered protocol, where dynamic parameters are introduced to adjust the threshold of event-triggered conditions. Compared with common attack detection methods relying on residuals between system and observer values, a novel and reliable mechanism for detecting DoS attacks is proposed, grounded in the logical relationship of voltage level signals derived from the outputs of detection components. Finally, a backstepping recursive design framework is utilized for constructing an eventtriggered adaptive fuzzy controller. Through the Lyapunov analysis, it is strictly demonstrated that, even under DoS attacks, the followers remain inside the leaders’ defined convex hull. The effectiveness of the presented control scheme is illustrated through the simulation results.

In this article, an adaptive reinforcement learning (RL) control problem is explored for a class of nonstrict-feedback discrete-time systems. First, different from the existing results, considering the noncausal problem which may exist in the backstepping design procedure, a universal system transformation method is first proposed for a class of nonstrict-feedback discrete-time systems. Second, by defining a compensation term to compensate the controller and utilizing the property of radial-basis-function neural network (RBFNN), an RL-based direct adaptive control strategy is developed via a backstepping method to achieve optimal control, and the multigradient recursive (MGR) algorithm is employed to estimate the weight vector. Finally, the stability of the control system is guaranteed and all signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB) on the basis of the Lyapunov theory. In addition, a universal system transformation is first proposed which breaks through the limitations on the controller design for the discrete-time nonstrict-feedback nonlinear system by using the traditional method. The validity of this strategy is verified by two simulation examples that include a course keeping system of the marine vessel.

In order to estimate the unmeasurable velocities of dynamic positioning system of vessels quickly and consider reducing energy consumption, modelling errors and the ocean unknown disturbances, this paper proposes an optimal adaptive anti-disturbance constrains control scheme via finite-time velocity observer. Firstly, a fuzzy logic system is utilized to approximate modeling errors, and the finite-time velocity observer is established to obtain velocities quickly. Then, a disturbance observer is used to estimate the unknown disturbances. An auxiliary system is designed to compensate the effects of constrains. Based on the obtained velocities and disturbances information, the whole controller is composed of the adaptive anti-disturbance controller and the optimal compensation term designed by adaptive dynamic programming. It can be proved that all the signals in the closed-loop system are bounded. At Last, the simulation results verify the effectiveness of the proposed optimal control scheme.

This paper investigates the problem of attack tolerant state estimation for nonlinear Cyber-Physical Systems (CPSs). Firstly, a fuzzy system is employed to model the nonlinear CPS, and the system dynamics are re-described according to the working region, taking into account the effects of network attacks and multi-source disturbances. Inspired by previous interval observers and unknown input observers, an unknown input interval observer is proposed in this paper. The observer can isolate decoupled disturbances and the resulting interval error augment system is stable and robust to the bounded attack, with the caveat that the system matrix of the interval error augmentation system must also be non-negative. After some mathematical manipulation, these performances can be guaranteed by a set of linear matrix inequality (LMI) based solvable conditions. Finally, the effectiveness of the proposed method is verified through a set of experiments.

Aiming at the impact of multi time scales characteristics of different energy sources in the CCHP cluster on the controller period, in this paper, a two-time-scale consensus control based on the multi-rate sampling mechanism is proposed. Firstly, a multi-rate sampling mechanism that includes a buffer and a zero order holder is established to achieve the function of controller updating as soon as the buffer receives sampled data. Due to improved data utilization, the dynamic performance of the system is better compared to the single rate sampling mechanism. Then, considering that the heating/cooling energy produced in CCHP cluster is mainly used for adjusting the indoor temperature, and electricity power mainly supplies for electrical loads. Hence, room temperature, power generation cost, and the angular frequency measuring the stability of electricity power system are taken as control objectives. The proposed two-time-scale consensus control method considering the multi time scales characteristics and coupling characteristics between different energy sources in CCHP cluster, can ensure stable and economic operation of the system. Finally, the simulation results of a CCHP cluster containing 4 CCHP devices demonstrate the effectiveness of the proposed method.

This paper focuses on the problem of optimal fuzzy output-feedback tracking control for dynamic positioning of marine vessels simultaneously with model uncertainties, unknown disturbances, unavailable velocities and thruster saturations. The control scheme is built by integrating a fuzzy velocity observer, a finite-time disturbance observer, a dynamic auxiliary system, the optimal control strategy with the dynamic surface control. The fuzzy velocity observer is constructed to obtain the unavailable velocities without requiring the certain model dynamics. The finite-time disturbance observer is established to estimate the unknown disturbances. The dynamic auxiliary system is designed to compensate for thruster saturations effects. By incorporating these methods, a disturbance rejection adaptive controller is designed with dynamic surface control. The optimal compensation term is inserted to minimize the cost function of the tracking error system by adaptive dynamic programming. It is shown that the optimal control consisting of the adaptive controller and the optimal compensation term guarantees that all signals in the closed-loop dynamic positioning system are bounded. Finally, simulation results demonstrate the effectiveness of the proposed optimal control scheme.

This article investigates the fault detection problem of unmanned marine vehicles (UMVs) under the influence caused by replay attacks. First, the dynamics of UMV are modeled by a Takagi--Sugeno (T--S) fuzzy system with an unknown membership function, which includes the nonlinear coupling of the internal state of the system, the environmental multisource disturbance as well as the potential thruster failure on UMV. Then, the possible replay attack from the sensor to the shore-based center is considered, and a switching-type attack tolerant fault detection filter is designed. Sufficient conditions are given to ensure that the filtering augmented system is stable and with stochastic finite frequency$H_{\infty }$and$H_{-}$performances, which reflect the robustness to the disturbance and sensitivity to the fault. On this basis, through a series of mathematical processing, the linear solvable conditions for the design of fault detection filters are obtained. Finally, the effectiveness of the proposed algorithm is verified by simulations.

In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H ∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed$l_{2}$gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.

This article presentsan adaptive fuzzy asymptotic disturbance rejection scheme for multiagent mechanical systems with unknown model dynamics and unknown frequency disturbances. The mechanical motion equations are expressed as the parameterized equations and the disturbances are described by unknown parametric exogenous system. The unknown parametric exogenous system is transformed into the canonical model with unknown disturbances as the inputs. The disturbance filter provides estimations of the state vector in the canonical model such that disturbances are represented as parameterized forms with exponentially decaying errors. The disturbance rejection can be converted into the adaptive control. Based on the adaptive vectorial fuzzy backstepping, the disturbance rejection formation controller is designed with the fuzzy logic systems approximating the model unknown dynamics. The adaptive robust control term attenuates fuzzy approximation errors. In contrast to the existing results, the adaptive fuzzy disturbance rejection formation control realizes the estimation and rejection for unknown frequency disturbances without requiring thea prioriknowledge of model dynamics. Based on the Barbalat’s lemma, it is shown that the adaptive disturbance rejection controller achieves the asymptotic formation tracking. Application on the surface vessel formation confirms the availability.

This paper is concerned with the asynchronous fault detection and isolation (FDI) strategy in finite frequency domain for Markov jump systems (MJSs) with actuator failures under networked environment. A set of binary-valued Bernoulli distributed sequences is introduced to describe the transmission and the scheduling of the sensor nodes. Then, an MJS directing by two Markov process and with multiple stochastic parameters is derived to characterize the whole system dynamics. Subsequently, by means of the stochasticH-index in finite frequency domain and the geometric mapping approach, an asynchronous FDI scheme is investigated, in which the filters complete the FDI mission with only partial information of the measurements. Meanwhile, each FDI filter is only sensitive to one possible actuator fault and decoupled from others. Then the existence of the desired filters is ensured by some novel sufficient conditions. Finally, an application example is presented to show the effectiveness of the proposed theoretical results.

This paper addresses a novel transmission-dependent fault detection and isolation (FDI) scheme in finite frequency domain for networked control systems with consideration of limited communication capacity, which includes multiple transmission intervals and delays, media accessing constraints as well as packet losses. By focusing on these phenomena, a switched stochastic system with multistochastic parameters is first modeled to represent the network-induced features. Then, with the aid of finite frequency stochastic performance index and geometric analysis method, a novel FDI scheme is proposed in finite frequency domain. The FDI filters are designed corresponding to the different transmission intervals and complete the task of detection and isolation only by partially available measurements. A novel lemma is investigated subsequently to capture the desired finite frequent stochastic performance for derived systems. Based on this lemma, sufficient conditions are developed to characterize the filter gains. Finally, an application to the VTOL aircraft is presented to show the effectiveness of the proposed approach.

In this paper, the problem of finite frequency filter design is addressed for networked control systems under multipacket transmission. The measured outputs are transmitted through a networked channel wherein time-varying delays would occur, also, only one packet can gain access to this channel in one transmission instant. In such a background, the considered systems are modeling into a switched systems with time-varying delays. Then, a definition of finite-frequency H∞index is subsequently presented to characterize the robustness to the disturbance in finite frequency domain and then an analysis condition to capture such a performance is derived originally. With the aid of this derived condition, a filter synthesis procedure is given in the framework of time domain inequalities. Finally, an example is given to illustrated the effectiveness of the proposed approach.