NEURAL LEARNING CONTROL METHODOLOGY FOR PREDEFINED-TIME SYNCHRONIZATION OF UNKNOWN CHAOTIC SYSTEMS

Qijia Yao, Qing Li, Ahmed Alotaibi, Hajid Alsubaie and Yu-Ming Chu
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Qijia Yao: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China†Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, P. R. China
Qing Li: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China†Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, P. R. China
Ahmed Alotaibi: ��Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Hajid Alsubaie: ��Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Yu-Ming Chu: �Institute for Advanced Study Honorning Chen Jian Gong, Hangzhou Normal University, Hangzhou 313000, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-11

Abstract: This paper presents a method for achieving synchronization of chaotic systems with unknown dynamics, using a predefined-time neural learning control approach. The proposed method includes a control law for synchronization and a parameter updating law that are designed to ensure stability according to the predefined-time Lyapunov theory. The analysis of stability indicates that the synchronization errors using this approach converge to a small region around zero within the predefined time. The effectiveness of the proposed method is demonstrated through simulation examples.

Keywords: Synchronization; Unknown Chaotic System; Predefined-Time Control; Neural Learning Control (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401461

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