Observer‐based event‐triggered H‐infinity sliding control of Markovian jump system suffer from actuator attacks

Abstract

This article investigates the issue of observer-based H ∞ sliding mode control for Markovian jump systems suffer from actuator attacks through an adaptive technique. During the communication channel from the plant output to estimator, a dynamic event-triggered generator is employed in enhancing communication efficiency. Taking consideration of malicious attacks on the plant actuator, an adaptive compensator is put forward for security purposes. By designing a state observer, the desired sliding mode dynamics can be derived based on an integral-type sliding surface. Further, maintaining the sliding motion with uncertain mode information is ensured in finite time by proposing a feasible sliding mode control law. In addition, both stochastic stability and H ∞ performance conditions are established for closed-loop systems in terms of linear matrix inequalities. Finally, a numerical example is offered to illustrate the validity of the constructed strategy.

State Estimation-Based Hybrid-Triggered Controller Design for Synchronization of Repeated Scalar Nonlinear Complex Dynamical Networks

“…2. As is shown in this figure, the trajectories of each node of RSNCDNs (39) are effectively synchronized to their respective isolated node trajectories which shows that the developed controller does its best to achieve the intended performance. Withal, it is evident from Figs.…”

“…For this purpose, we compute the observability matrix Obs with the aid of MATLAB toolbox from the system matrices A and J chosen and is obtained as After which, in order to move forward with the simulation testing, we choose the necessary parameters listed herein. The timevarying coupling delay d(t) in (39) with bound [0, 0.1] is picked as d(t) = 0.05 + 0.05 sin(t), the external disturbance functions are taken as wi(t) = 0.2(sin(t) + sin(πt)), i = 1, 2, . .…”

“…Alongside the above specified information in hand and the initial conditions ξ1(t0) = ξ1(t0) = [8 6 8] T , ξ3(t0) = ξ3(t0) = [5 6 5] T , s(t0) = ξr(t0) = ξr(t0) = [−10 2 − 10] T , r = 2, 4, 5, with sampling period 0.01, the simulation outcomes are illustrated in the Figures 2-9. The effective synchronization responses of the three trajectories of RSNCDNs (39) under the intended controller is disclosed in Fig. 2.…”

Robust H∞ sliding mode control scheme for uncertain fractional stochastic systems: Nonlinear analysis and design

“…External disturbances, variations in the parameters, and matched or mismatched uncertainties are crucial to be factored in the design of the controller in order to achieve a favorable closed-loop response. SMC as a wellestablished approach to handle the aforementioned issues has been applied for controller design for stochastic systems [9][10][11]. In [11], an exponential mean square stabilization scheme for fractional stochastic systems driven by fBm has been proposed.…”

Hidden Markov model‐based asynchronous exponential stabilization for stochastic Markovian jump systems with incomplete controller mode information

“…Under the circumstances, these systems can be described as Markovian jump systems (MJSs), which are composed of both time-evolving and event-driven mechanisms. In general, an MJS is composed of several subsystems subject to a stochastic signal obeying Markov process, whereupon great efforts have been made for MJSs, yielding many results on stability and stabilization [1][2][3], controller design [4][5][6], and observer design [7,8].…”