Zero‐sum game‐based security control of unknown nonlinear Markov jump systems under false data injection attacks

Abstract

In order to solve the security control problem of discrete-time unknown Markov jump systems (MJSs) subject to false data injection attacks, a new zero-sum game-based control method is developed in this paper by using model-free adaptive dynamic programming algorithm. The controller and the attacker are regarded as two players. An optimal cost function of MJSs is firstly introduced based on the evolution of the Markov chain. Then, a critic-action-attack framework is established to approximate the cost function, control input and false data, respectively. Additionally, the boundedness of the neural network (NN) weight estimate errors is guaranteed via Lyapunov theory. In the end, the validity of the proposed approach is verified by simulation of a single link robot arm.