This technical note investigates the problem of extended dissipative finite-time control for Markov jump systems (MJSs) with cyber-attacks and actuator failures. A probabilistic event-triggered mechanism (PETM) is proposed to relieve the communication burden by exploiting both the pattern variation of triggering thresholds and the time-varying characteristic of transmission delays. To characterize the actual control inputs, a stochastic actuator failure model (SAFM) is established using a random variable of any discrete-time distribution over [0,1]. Firstly, based on the PETM and SAFM, static output feedback controllers are devised which may not switch with the system synchronously. Then, novel sufficient conditions with less conservatism are obtained to achieve the extended dissipative finite-time control performance of the closed-loop system under admissible cyber-attacks and actuator failures. Furthermore, controller gains with non-convex constraints are calculated with the aid of a newly proposed lemma. Finally, an application oriented example is provided to verify the effectiveness and superiority of the proposed results.

This article investigates the finite-time secure filter design of fuzzy switched cyber-physical systems equipped with a resource-constraint network that may undergo false data injection attacks (FDIAs). To strike a higher level balance between the resource consumption and filtering performance, a multidomain probabilistic event-triggered mechanism (MDPETM) is initially developed. And the mode mismatched phenomenon between the filter and the system is characterized through a delayed switching signal. Based on the MDPETM and a virtual delay partitioning approach, fuzzy mismatched secure filters are first devised whose modes could differ from the system. Then, filter-mode-dependent Lyapunov functionals are created to obtain new sufficient criteria such that the filtering error achieves finite-time boundedness with extended dissipativity subject to admissible FDIAs. The filter gains are obtained by solving a set of convex optimization problems. Finally, an application-oriented example is employed to test the effectiveness and advantages of the proposed results.

This paper investigates the attack-compensated output control problem for a class of Markov jump cyber-physical systems (MJCPSs) subject to mismatched modes. A sub-domain-awareness adaptive probabilistic event-triggered mechanism (APETM) is innovatively developed to fully enhance the control performance of the networked control system. Here, sub-domain-awareness means the network’s communication ability, which may be affected by time delay and network resource. To defend against the cyber-attacks imposed on the communication channel, a predictor-based compensator is constructed to mitigate the impact of attacks on the control performance. Different from the existing works, the mismatch degree between the controller and the system, governed by a random variable obeying any discrete-time distribution over $[0, 1]$ , is changing dynamically. A mismatched output feedback controller is designed based on the APETM and attack-compensator, and an augmented closed-loop system is obtained. Both time delay-and triggering threshold-dependent Lyapunov functionals are introduced to perform the asymptotical stability analysis. With a separation technique, a solving algorithm is provided which converts the non-convex analysis conditions into traceable ones. Finally, simulations are conducted to validate the proposed results through a mass-spring-damping system model. Note to Practitioners —Cyber-physical systems (CPSs) have found many applications in the fields such as smart grid, gas distribution systems, and automated manufacturing systems. The open communication network deployed in CPSs face two main challenges including the security of data transmission and the efficiency of resource utilization. Owing to the practical environment and other factors, system parameters may change abruptly, which can be well defined by a Markov model. In the resultant Markov jump system, it is typically assumed that the mode mismatch degree between the controller and the system remains the same, which is unrealistic in a time-varying network environment. Motivated by these observations, this paper investigates the secure output control of Markov jump CPSs (MJCPSs) subject to mismatched modes and unknown cyber-attacks. An adaptive probabilistic event-triggered mechanism is innovatively proposed, which compared with the existing mechanisms strikes a higher level balance between the communication burden and the control performance. And attack-compensated mismatched output controllers are developed to stabilize MJCPSs and meanwhile preserve the security level. Finally, a mass-spring-damping system is adopted to verify the application effectiveness of the proposed results. Note that communication traffic control and secure control are finely involved based on CPSs, making our proposed results more applicable to practical systems.

This paper investigates the H state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H _∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H _∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.

This article addresses the finite-time dissipative fuzzy state estimation for Markov jump systems under mixed cyber attacks. A probabilistic event-triggered mechanism (PETM) is proposed to reduce the unwanted network traffic by using the statistic information of network-induced delays. The dual asynchronizations characterized by asynchronous modes and mismatched premise variables are tackled simultaneously. Under the PETM, Takagi–Sugeno (T–S) fuzzy state estimators are first constructed based on the imperfect measurements subject to mixed cyber attacks and exogenous disturbances. Less conservative criteria relying on both fuzzy rules and jumping modes are established to achieve the strictly $(\mathcal {Q},\mathcal {S},\mathcal {R})$ - $\vartheta $ -dissipative finite-time state estimation performance. Furthermore, a synthesis algorithm is derived to calculate the fuzzy state estimator gains by virtue of an improved matrix decoupling technique. Finally, two examples are utilized to validate the effectiveness and advantage of the proposed results.

This paper studies the observer-based event-triggered neural asynchronous control problem of nonhomogeneous Markov jump power systems (MJPSs) under hybrid cyberattacks. Both periodic denial-of-service attacks and deception attacks (DAs) are involved because of the openness of networks. An event-triggered mechanism is established to ease the network load. Besides, neural network (NN) approaches are adopted to suppress the adverse effects of DAs on nonhomogeneous MJPSs, and the hidden Markov model (HMM) is employed to capture the asynchronous phenomenon between the system mode and the controller mode. With NN approaches and HMM, an observer-based NN asynchronous controller is designed. By constructing the mode- and parameter-dependent Lyapunov function, sufficient criteria ensuring boundedness in probability for nonhomogeneous MJPSs are obtained. Then, a design algorithm is shown to simultaneously obtain the controller gains, observer gains, and event-triggering weight matrices based on the established conditions. Finally, the single-machine infinite-bus power system and 3-machine 6-bus power system are applied to justify the validity of the proposed algorithm.