Improved event-triggered finite-time H_∞ control for neural networks subject to mixed-type communication attacks

ABSTRACT

This paper discusses the problem of finite-time $H_{\mathrm{\infty }}$ stabilisation for neural networks (NNs) subject to mixed-type communication attacks via an improved dynamic event-triggered scheme (DETS). The complex cyber-attacks considered consist of three common types of attacks: replay attacks, deception attacks, and denial-of-service (DoS) attacks. Different from most articles which use independent Bernoulli variables to model the cyber-attacks, this paper considers these attacks into a unified Markovian jump framework for modelling. In order to save the limited network communication resources, the improved DETS is adopted. An appropriate Lyapunov–Krasovskii functional (LKF) containing the proposed improved DETS condition is constructed, and sufficient conditions are obtained to guarantee finite-time $H_{\mathrm{\infty }}$ stabilisation of the system. Then, according to a set of feasible linear matrix inequalities (LMIs), the co-design of event-trigger and $H_{\mathrm{\infty }}$ controller is given. Finally, two numerical examples are provided to demonstrate the effectiveness of our method.

KEYWORDS:

• Finite-time H_∞ stabilisation
• dynamic event-triggered scheme
• neural networks
• mixed-type communication attacks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 61573095].