Estimate-Based Adaptive Neural Secure Control for Nonminimum-Phase Nonlinear Systems With Hybrid Attacks via Dynamic Event-Triggered Scheme

This paper investigates the estimate-based adaptive neural secure control method for nonminimum-phase nonlinear networked control systems (NNCSs) under hybrid attacks via dynamic event-triggered scheme. Hybrid attacks such as denial-of-service (DoS) attacks and false data injection (FDI) attacks are assumed to occur in the sensor-to-controller (STC) channel and controller-to-actuator (CTA) channel. When only the system output is obtained, a sampled-data observer is introduced to estimate unknown system states. In order to improve the utilization of communication bandwidth under DoS attacks, a dynamic event-triggered scheme (DETS) is proposed to save network resources. An event-triggered output feedback controller is designed by utilizing neural network (NN) to approximate FDI signals. Based on the proposed dynamic event-triggered control strategy, the selections of the sampling period and the observer gain are derived to ensure the boundedness of the control systems. At last, the efficiency of the control strategy is verified by a numerical example.