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Neural Learning Control Methodology For Predefined-Time Synchronization Of Unknown Chaotic Systems

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qijia Yao & Qing Li & Ahmed Alotaibi & Hajid Alsubaie & Yu-Ming Chu, 2023. "Neural Learning Control Methodology For Predefined-Time Synchronization Of Unknown Chaotic Systems," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-11.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x23401461
    DOI: 10.1142/S0218348X23401461
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