This paper studies the problem of event-triggered sliding mode control of discrete-time Markov jump systems (MJSs). Two kinds of classical control schemes, which are observer-based control and state-feedback control schemes, are employed to handle the proposed synthesis problem. The event-triggered observer-based sliding mode controller and event-triggered state-feedback sliding mode controller are established by plunge of discrete-time event detectors into the studied control system, respectively. Moreover, the proposed event-triggered sliding mode controllers can guarantee the MJSs to be stochastically stable with H _∞ performance, and ensure the finite-time reachability of the specified sliding manifold. Simulation results are provided to illustrate the effectiveness of the proposed theoretical results.

The problem of finite-horizon H _∞ state estimator design for periodic neural networks over multiple fading channels is studied in this paper. To characterize the measurement signals transmitted through different channels experiencing channel fading, a multiple fading channels model is considered. For investigating the situation of correlated fading channels, a set of correlated random variables is introduced. Specifically, the channel coefficients are described by white noise processes and are assumed to be correlated. Two sufficient criteria are provided, by utilizing a stochastic analysis approach, to guarantee that the estimation error system is stochastically stable and achieves the prescribed H _∞ performance. Then, the parameters of the estimator are derived by solving recursive linear matrix inequalities. Finally, some simulation results are shown to illustrate the effectiveness of the proposed method.

This paper studies the distributed state estimator design method for periodic systems with time-varying delays and periodic communication scheduling strategy over sensor networks. To mitigate the stability attenuation and the performance loss caused by delays, a periodic delay-dependent Lyapunov function is constructed, which relaxes the conservatism and improves the ${H_\infty }$ performance of the estimators. Then, a periodic scheduling strategy, which updates the correlative innovations of each node during a period in an average way by solving an integer programming problem, is proposed to save energy. Periodic gains of distributed estimators are designed by using analog tool. Finally, a visual example is utilized to describe the periodic communication scheduling. The effectiveness of the strategy and the novel Lyapunov function is demonstrated by the example.

This article is concerned with a hybrid distributed dynamic state estimation (DSE) algorithm for large-scale power grids. Based on the mixed phasor measurement unit (PMU) and remote terminal unit measurements model, a modified distributed Kalman filter (KF) is designed. Different from the centralized KF algorithm, the distributed approach is capable of independently estimating local states by local measurements. Moreover, in each local region, the multiple missing measurements problem is considered in the modified distributed KF algorithm design. The internodal transformation theory is employed to deal with the communication problem between the distributed subsystems. Therefore, the proposed method can reduce the communication latency while ensuring the estimation accuracy. Considering the inequality constraints, the particle swarm optimization algorithm and the probability-maximization method are applied to tackle the corresponding constrained estimation issue. The proposed distributed DSE algorithm is tested on an IEEE benchmark 14-bus system to demonstrate its effectiveness and applicability.

This article investigates the problem of adaptive neural sliding mode control for Markov jump systems. The transition probabilities of the Markov process are partly unknown. The cyber layer, which is vulnerable to the adversary, is deployed to broadcast the control signal to the actuator. The attacker can inject malicious information to the control signal to deteriorate the system performance. A sliding mode controller is designed to stabilize the system. Then, sufficient conditions that ensure the stability of the closed-loop system are given in the framework of the Lyapunov theory. In the end, two simulation examples are applied to illustrate the effectiveness and feasibility of the proposed methodology.