Summary This paper investigates the resilient control problem for constrained continuous‐time cyber‐physical systems subject to bounded disturbances and denial‐of‐service (DoS) attacks. A sampled‐data robust model predictive control law with a packet‐based transmission scheduling is taken advantage to compensate for the loss of the control data during the intermittent DoS intervals, and an event‐triggered control strategy is designed to save communication and computation resources. The robust constraint satisfaction and the stability of the closed‐loop system under DoS attacks are proved. In contrast to the existing studies that guarantee the system under DoS attacks is input‐to‐state stable, the predicted input error caused by the system constraints can be dealt with by the input‐to‐state practical stability framework. Finally, a simulation example is performed to verify the feasibility and efficiency of the proposed strategy.

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The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault-tolerant control algorithms are proposed for non-Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H 1 performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault-tolerant controller is designed to make the post-fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault-tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm.Y. SUN AND L.YAO With the study of fault diagnosis of non-Gaussian SDC systems, some fault diagnosis algorithms have been proposed. For fault diagnosis of SDC systems, in which the information of system output PDF and other measured information to generate residuals in order to analyze and estimate the change of fault, observer or filter-based methods are mainly used so far [5][6][7][8][9][10][11][12][13]. A stable filterbased residual generator is constructed such that the fault can be detected and diagnosed for general stochastic systems in [5]. In [6], a nonlinear neural network observer is designed for fault diagnosis in which the adaptive tuning rule for network parameters is determined by the Lyapunov stability theorem. In [7], a fault diagnosis algorithm is proposed based on iterative learning observer for SDC systems. In [8], a fault diagnosis method based on robust filters is proposed for time-delayed SDC systems, and a robust H 1 fault diagnosis scheme is presented in [9]. Otherwise, in [12], a high-gain nonlinear observer-based fault diagnosis approach is proposed for a class of nonlinear uncertain systems with measurable output PDFs. In [13], a novel fault-estimation observer is designed for Takagi-Sugeno (T-S) fuzzy systems with actuator faults, and the problem of fault-tolerant control is addressed. For fault-tolerant control, the active fault-tolerant control is mainly used so far. Combined with the controller design method without fault, such as optimal control, PI control, sliding mode control [14], robust control, iterative learning control, model reference adaptive control, etc, the controller can be reconfigured or reconstructed when fault occurs. In [15], a neural network-based active fault-tolerant control scheme with fault alarm is proposed for a class of nonlinear systems....

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