Multi-event triggered sliding mode control for a class of complex neural networks. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Guangchen; Li, Xufei; Xia, Yuanqing
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Neural network -- Multi-sliding mode control -- Multi-event triggered mechanism -- Sliding mode control algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107506
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper considers the sliding mode control issue for the complex neural network coupled with multi-interconnected neurons. Firstly, the stability of the neural network is realized by designing a multi-event triggered mechanism and a multi-sliding surface. In addition, the corresponding stability criterion is proposed with solvable matrix constraints. Secondly, a multi-sliding mode control law is developed based on a multi-event triggered mechanism, and then sliding mode control synthesis and reachability issues are formulated for the corresponding closed-loop neural network. Based on these discussions, an innovative sliding mode control algorithm is designed to achieve the sliding mode gain and control law. To conclude the paper, an example is provided to illustrate the feasibility and validity for the proposed methods and criteria. Highlights: The stability analysis of the neural network system is realized by designing multi-event triggered mechanism and multi-sliding surface. Multi-sliding mode control law is developed based on the multi-event triggered mechanism. The sliding mode control synthesis and reachability are formulated for the corresponding closed loop neural network. Graphical abstract: