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Practical fixed-time neural control for MIMO non-strict feedback nonlinear systems: an adaptive neural network approach

Author

Listed:
  • Wenshan Bi
  • Shuai Sui
  • Shaocheng Tong
  • C. L. Philip Chen

Abstract

This paper studies the non-singular practical fixed-time neural adaptive control issues for multi-input and multi-output (MIMO) nonlinear systems with non-strict feedback form. Neural networks (NN) are used to estimate the unknown nonlinearities and deal with the problem of an algebraic loop. Under the framework of the backstepping control design, a practical fixed-time adaptive NN control method is developed by using the adding power integration technology. According to the Lyapunov function theory, it is proved that the closed-loop system is practical fixed-time stable, and the system can track the desired reference signal within a fixed time. Finally, the proposed practical fixed-time control method is applied to a multi-motor control platform, which proves the effectiveness of the control method.

Suggested Citation

  • Wenshan Bi & Shuai Sui & Shaocheng Tong & C. L. Philip Chen, 2024. "Practical fixed-time neural control for MIMO non-strict feedback nonlinear systems: an adaptive neural network approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(11), pages 2325-2336, August.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:11:p:2325-2336
    DOI: 10.1080/00207721.2024.2343740
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