Event-Triggered Adaptive Neural Prescribed Performance Tracking Control for Nonlinear Cyber–Physical Systems against Deception Attacks

Abstract

This paper investigates the problem of the adaptive neural network tracking control of nonlinear cyber–physical systems (CPSs) subject to unknown deception attacks with prescribed performance. The considered system is under the influence of unknown deception attacks on both actuator and sensor networks, making the research problem challenging. The outstanding contribution of this paper is that a new anti-deception attack-prescribed performance tracking control scheme is proposed through a special coordinate transformation and funnel function, combined with backstepping and bounded estimation methods. The transient performance of the system can be ensured by the prescribed performance control scheme, which makes the indicators of the controlled system, such as settling time and tracking accuracy, able to be pre-assigned offline according to the task needs, and the applicability of the prescribed performance is tested by selecting different values of the settling time (0.5 s, 1 s, 1.5 s, 2 s, 2.5 s, and 3 s). In addition, to save the computational and communication resources of the CPS, this paper uses a finite-time differentiator to approximate the virtual control law differentiation to avoid “complexity explosion” and a switching threshold event triggering mechanism to save the communication resources for data transmission. Finally, the effectiveness of the proposed control strategy is further verified by an electromechanical system simulation example.