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Integral variable structure control of nonlinear system using a CMAC neural network learning approach.

Chin-Pao Hung1

  • 1Department of Electrical Engineering, National Chin-Yi Institute of Technology, Taiping City, Taiwan, ROC. c.p.hung@seed.net.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
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This study introduces a novel integral variable structure control (IVSC) using a CMAC neural network and soft supervisor for nonlinear systems. The new method ensures global stability and eliminates control signal chattering, improving system performance.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Nonlinear systems present significant control challenges due to their complex dynamics.
  • Traditional control methods often struggle with parameter uncertainties and control signal chattering.
  • Integral Variable Structure Control (IVSC) offers a framework for robust control but can be complex to implement.

Purpose of the Study:

  • To develop a novel Integral Variable Structure Control (IVSC) scheme for Single-Input Single-Output (SISO) nonlinear systems.
  • To integrate a Cerebellar Model Articulation Controller (CMAC) neural network with a soft supervisor for enhanced control.
  • To guarantee global stability and eliminate control signal chattering in nonlinear systems.

Main Methods:

Related Experiment Videos

  • A novel IVSC scheme combining CMAC neural network and a soft supervisor controller was designed.
  • Lyapunov theorem was employed to ensure the global stability of the soft supervisor controller.
  • A real-time learning algorithm within the CMAC facilitated the equivalent control aspect of the IVSC.
  • The proposed CMAC-based IVSC (CIVSC) scheme was developed to reduce parameter dependency and chattering.

    • Main Results:

    • The CIVSC scheme demonstrated global stability, with all system signals bounded.
    • The tracking error was shown to converge to zero, indicating precise control.
    • The controller exhibited reduced dependency on system parameters.
    • Numerical simulations confirmed the effectiveness and robustness of the proposed control strategy.

    Conclusions:

    • The proposed CMAC-based IVSC (CIVSC) effectively controls SISO nonlinear systems.
    • The integration of CMAC and soft supervisor ensures global stability and eliminates chattering.
    • The controller's robustness and parameter independence are significant advantages for practical applications.