Event‐Triggered Secure Consensus for Cyber‐Physical Systems With Dynamic Quantized Measurement

ABSTRACT This paper investigates the robust secure consensus control of nonlinear cyber‐physical systems with external disturbances in the presence of actuator and sensor attacks. In the process of information transmission, it is considered that malicious attackers affect the consensus by destroying the integrity of control signals and the communication channel between agents. Then, a dynamic quantizer with a limited quantization level is constructed according to the zoom‐out/zoom‐in adjustment policy of the quantizer sensitivity parameter, and the unknown nonlinear function of the follower is approximated by the fuzzy neural network (FNN). A novel event‐triggered adaptive FNN quantization sliding mode control scheme is proposed, and the quantized event‐triggered state is applied in the controller design. In addition, the controller gain is obtained by solving a linear matrix inequality (LMI), and the positive lower bound of the event interval is ensured to exclude Zeno behavior. Finally, a numerical example illustrates the effectiveness of the algorithm.