Security Control of Nonlinear Systems Subject to Deception Attacks: A Reinforcement Learning Approach

Abstract

Dear Editor, This letter deals with the security control for nonlinear cyber-physical systems (CPSs) under mixed deception attacks. Both sensors and actuators are assumed to be injected deception data during the data transmission via networks. In order to identify the unknown dynamics of the attacked system, a neural network (NN) is adopted, on basis of which an NN-based secure observer is designed to diminish the attack impact on state estimation. Then, by resorting to the reinforcement learning approach, the secure control strategy is presented via actor-critic and zero-sum games. At last, the designed control scheme is proved via a numerical simulation.