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Adaptive recurrent neural control for nonlinear system tracking.

E N Sanchez1, M A Bernal

  • 1CINVESTAV-IPN, Mexico City, Mexico.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces a novel indirect adaptive control law using recurrent neural networks for stable system identification and tracking. Simulations demonstrate the effectiveness of this new control scheme in achieving asymptotic stability for tracking errors.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Adaptive control systems are crucial for dynamic environments.
  • Recurrent neural networks (RNNs) offer powerful modeling capabilities.
  • Sliding mode control (SMC) provides robust control solutions.

Purpose of the Study:

  • To develop a new indirect adaptive control law.
  • To integrate recurrent neural networks (linear on input) for system identification.
  • To ensure asymptotic stability of tracking errors.

Main Methods:

  • Utilizing a recently published algorithm adapted for RNN-based identification.
  • Ensuring exponential stability for the identification error.
  • Designing the controller based on sliding mode techniques.

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Main Results:

  • A novel indirect adaptive control law is proposed.
  • The identification algorithm guarantees exponential stability for identification error.
  • The main theorem proves asymptotic stability for tracking error.

Conclusions:

  • The proposed indirect adaptive control scheme demonstrates effective tracking.
  • The integration of RNNs and sliding mode control is viable.
  • Simulation results validate the applicability of the control scheme.