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This study introduces a hybrid control system combining a neural network controller and a cerebellar model articulation controller (CMAC) for dynamic time-varying plants. This approach simplifies CMAC design and ensures system stability, proving effective in simulations.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Dynamic time-varying plants present significant control challenges.
  • Existing control methods may struggle with complexity and adaptability.
  • Hybrid approaches offer potential for enhanced performance.

Purpose of the Study:

  • To propose a novel hybrid control method for dynamic time-varying plants.
  • To integrate a neural network controller with a cerebellar model articulation controller (CMAC).
  • To simplify CMAC design and ensure system stability.

Main Methods:

  • A hybrid controller combining a neural network and CMAC is developed.
  • The neural network controller preprocesses input signals.
  • Adaptive laws derived via steepest-descent and back-propagation adjust network parameters.
  • Lyapunov stability theory guarantees system convergence.

Main Results:

  • The hybrid method effectively controls dynamic time-varying plants.
  • The neural network reduces input range and quantity for the CMAC.
  • The CMAC structure is simplified, easing network size and membership function design.
  • Numerical simulations confirm the proposed method's effectiveness.

Conclusions:

  • The proposed hybrid neural network-CMAC controller is effective for dynamic time-varying plants.
  • This architecture simplifies CMAC design and ensures stability.
  • The method offers a robust solution for complex control problems.