Adaptive integral sliding‐mode control strategy of data‐driven cyber‐physical systems against a class of actuator attacks

This study is concerned with the reliable and optimal control problems of data‐driven cyber‐physical systems (CPSs) against a class of actuator attacks. Consider an unknown continuous‐time linear physical system with the external disturbance, and it is assumed that control input signals transmitted via network layers are vulnerable to cyber attacks. By introducing a new integral sliding‐mode function and utilising the available data acquired by an off‐policy reinforcement learning algorithm, a novel data‐based adaptive integral sliding‐mode control strategy is presented. Different from the existing control policies, the novel one uses a data‐driven sliding‐mode compensator to eliminate the effect of the actuator attacks such that the stability and a nearly optimal performance of the CPSs can be guaranteed. Finally, the effectiveness of the proposed control strategy is verified by a numerical example.