Adaptive Neural Sliding Mode Control of Markov Jump Systems Subject to Malicious Attacks
Wenshuai Lin, Bin Zhang, Deyin Yao, Hongyi Li, Renquan Lu
Abstract
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.