Improved event-triggered finite-time H ∞ control for neural networks subject to mixed-type communication attacks - Xu, Yao; Lu, Hongqian; Song, Xingxing; Zhou, Wuneng (International journal of control, 2023)
This paper discusses the problem of finite-time 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 stabilisation of the system. Then, according to a set of feasible linear matrix inequalities (LMIs), the co-design of event-trigger and controller is given. Finally, two numerical examples are provided to demonstrate the effectiveness of our method.