Neural network-based adaptive event-triggered control for cyber–physical systems under resource constraints and hybrid cyberattacks

Abstract

In this paper, the neural network (NN)-based adaptive event-triggered control (AETC) strategy is developed for cyber–physical systems (CPSs) under resource constraints and hybrid cyberattacks. First, by considering the nonperiodic denial-of-service (DoS) attacks that only constrain their frequency and duration and removing the strict assumption on the malicious information injected by deception attacks in existing literature, an unexplored hybrid cyberattack model is proposed. Second, a novel NN-based adaptive event-triggered state-feedback controller is designed, which not only flexibly solves the resource constraints of CPSs while considering the adverse effects of DoS attacks, but also adopts an on-line weight-adjusted NN to approximate and mitigate the malicious information injected by deception attacks. Then, the switched delay system model is established for the closed-loop control system with the designed novel controller. By carefully constructing a switched Lyapunov–Krasovskii functional and using the switched delay system theory and NN technology, the explicit form of the designed novel controller is obtained, which can ensure that the closed-loop control system is exponentially asymptotically stable (EAS). Finally, a numerical example is given to illustrate the effectiveness of the proposed strategy.