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Adaptive CMAC-based supervisory control for uncertain nonlinear systems.

Chih-Min Lin1, Ya-Fu Peng

  • 1Department of Electrical Engineering, Yuan-Ze University, 320 Taiwan, ROC. cml@ee.yzu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 21, 2004
PubMed
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A novel adaptive cerebellar-model-articulation-controller (CMAC) supervisory control system effectively manages uncertain nonlinear systems. This robust control ensures system stability and state constraints, demonstrated in robotic and electronic applications.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Uncertain nonlinear systems pose significant control challenges.
  • Existing control methods may struggle with approximation errors and stability guarantees.
  • Adaptive control strategies are crucial for dynamic system management.

Purpose of the Study:

  • To develop an adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control system.
  • To ensure system state stability within predefined constraints for uncertain nonlinear systems.
  • To guarantee system stability using Lyapunov functions and estimate approximation error bounds.

Main Methods:

  • An adaptive CMAC controller mimics ideal control laws.
  • A supervisory controller enforces system state constraints.

Related Experiment Videos

  • Lyapunov stability analysis and adaptive laws ensure system robustness.
  • An estimation law relaxes the need for approximation error bounds.

    • Main Results:

    • The adaptive CMAC compensates for approximation errors.
    • The supervisory controller actively corrects deviations from state constraints.
    • Lyapunov-based adaptive laws guarantee system stability.
    • An estimation law effectively bounds approximation errors, enhancing adaptability.

    Conclusions:

    • The proposed adaptive CMAC-based supervisory control system is effective for uncertain nonlinear systems.
    • The control scheme ensures stability and state constraints.
    • Demonstrated success in controlling robotic manipulators, chaotic circuits, and LPCM devices.