Prescribed performance adaptive neural event-triggered control for switched nonlinear cyber–physical systems under deception attacks

Abstract

In this paper, the design of an adaptive neural event-triggered control scheme for a class of switched nonlinear systems affected by external disturbances and deception attacks is presented. In order to address the effects caused by unknown disturbances, a switched nonlinear disturbance observer is used, and the error between the estimated signals and actual disturbances is small. Meanwhile, a prescribed performance function is introduced, which aims to ensure system output reaches the performance bounds within a predefined finite time. In addition, a dynamic event-triggered mechanism is designed to reduce the communication load. Based on the theoretical analysis, all signals within the closed-loop system are bounded, while simultaneously ensuring the complete elimination of Zeno behavior. Finally, the validity and efficacy of the scheme are proven by an example of numerical simulation.