Predictor-based event-triggered learning control of networked control systems with false data injection attacks and output delay

This article is concerned with the predictor-based event-triggered learning control of networked control systems (NCSs) with false data injection attacks (FDIAs) and output delay. Firstly, by applying the prediction method, a new state observer including an output predictor is employed to get the estimation of delayed sampled-data output in the context of sampling. To improve the efficiency of limited networked resources, an intelligent periodic event-triggered scheme (PETS) is established, in which the triggered threshold can be optimized by the asynchronous advantage actor-critic (A3C) algorithm. Then, a predictor-based event-triggered learning control strategy is developed to handle the FDIAs occurring in the controller-to-actuator channel, and the neural network (NN) technique is introduced to approximate the false data. By applying the Lyapunov function, some sufficient conditions are given to guarantee the boundedness of the NCSs. At last, a simulation of a satellite system is given to confirm the superiorities of the presented predictor-based learning control strategy.