Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach

Yuru Guo; Chang Liu; Yonghua Liu; Yong Xu; Renquan Lu; Tingwen Huang

doi:10.1016/j.neunet.2023.03.002

Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach

Neural Netw. 2023 May:162:288-296. doi: 10.1016/j.neunet.2023.03.002. Epub 2023 Mar 8.

Authors

Yuru Guo¹, Chang Liu², Yonghua Liu³, Yong Xu⁴, Renquan Lu⁵, Tingwen Huang⁶

Affiliations

¹ Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: guo_yuru0626@163.com.
² Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: liuchangwm@163.com.
³ Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: yonghua.liu@outlook.com.
⁴ Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: xuyong809@163.com.
⁵ Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: rqlu@gdut.edu.cn.
⁶ Science Program, Texas A&M University at Qatar, Doha 23874, Qatar. Electronic address: tingwen.huang@qatar.tamu.edu.

PMID: 36933514
DOI: 10.1016/j.neunet.2023.03.002

Abstract

This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.

Keywords: Adaptive control; Bounded synchronization; Impulsive control; Master–slave neural networks.

MeSH terms

Algorithms*
Neural Networks, Computer*
Time Factors
Uncertainty