Dynamic quantization of event-triggered adaptive sliding mode control for networked control systems under false data injection attack

Abstract

The dynamic quantization of event-triggered (ET) adaptive sliding mode control (SM, SMC) for networked control systems (NCS) under false data injection attack (FDIA) is considered in this article. To begin with, to reduce the network transmission burden, dynamic quantizers are used to quantize the states and the input on the channels from the plant to the ET mechanism and from the controller to the plant, respectively. Secondly, the dynamic ET mechanism employs quantized state error, and the existence of the minimum inter-event time demonstrates that the system does not experience the Zeno phenomenon. Thirdly, this paper uses the adaptive parameter to estimate the unknown upper bound of the attack mode. In addition, the range of values for the adaptive gain of the SMC is derived by combining with the Lyapunov stability theory. On the last, the comparative simulation results of different methods for numerical examples are given to verify the superiority of the method proposed in this paper.