Master–slave synchronization of neural networks via event-triggered dynamic controller

• Published in: Neurocomputing (Amsterdam) Vol. 419; pp. 215 - 223
• Main Authors: Wang, Yong, Ding, Sanbo, Li, Ruoxia
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 02.01.2021
• Subjects: Event-triggered dynamic controller; Input saturation; Master–slave synchronization; Neural networks; Input saturation; Event-triggered dynamic controller; Master–slave synchronization; Neural networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.08.062

This paper investigates the event-triggered dynamic feedback control for master–slave synchronization of neural networks with actuator saturation. Firstly, a novel event-triggered mechanism with an exponentially decaying term is proposed. It reduces the value of measure function, which corresponds to decrease the event-triggered frequency. Secondly, a dynamic feedback controller is designed to synchronize the neural networks. In this way, the control input is described as a specific time-varying signal between two consecutive event-triggered instants. Thirdly, by constructing Lyapunov functional and utilizing the generalized sector condition, some sufficient synchronization criteria are established via linear matrix inequalities. Whereafter, the co-designed problem of control gains and triggering parameters is discussed. The rigorous mathematical analysis is provided to show that the presented event-triggered mechanism can eliminate the Zeno behavior. Finally, a numerical example is employed to illustrate the effectiveness of the proposed approach.