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Composite adaptive control with locally weighted statistical learning.

Jun Nakanishi1, Jay A Farrell, Stefan Schaal

  • 1ATR Computational Neuroscience Laboratories, Department of Humanoid Robotics and Computational Neuroscience, 2-2 Hikaridai, Seiko-cho, Soraku-gun, Kyoto 619-0288, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|January 15, 2005
PubMed
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This study presents a stable learning adaptive control framework using statistical learning. The novel approach automatically grows neural networks to precisely approximate complex system dynamics, ensuring accurate control.

Area of Science:

  • Control Engineering
  • Machine Learning
  • Nonlinear System Analysis

Background:

  • Adaptive control systems require accurate models of system dynamics.
  • Existing methods often struggle with highly nonlinear or time-varying systems.
  • Online adaptation and function approximation are crucial for robust control.

Purpose of the Study:

  • Introduce a novel, probably stable learning adaptive control framework.
  • Develop an algorithm capable of automatic network growth for nonlinear function approximation.
  • Enhance control accuracy and convergence speed in dynamical systems.

Main Methods:

  • Utilizes statistical learning and nonparametric regression for function approximation.
  • Employs a locally weighted learning framework with piecewise linear models.

Related Experiment Videos

  • Incorporates both tracking and estimation errors for online parameter optimization.
  • Includes a parameter projection method to ensure stability during adaptation.

    • Main Results:

    • The proposed learning adaptive control framework demonstrates probable stability.
    • The algorithm effectively approximates unknown nonlinear functions in dynamical systems.
    • Numerical simulations show rapid convergence and high control accuracy.
    • The method is extendable from SISO to MIMO systems.

    Conclusions:

    • The developed learning adaptive control framework offers a robust solution for complex systems.
    • Automatic network growth and online parameter optimization enhance adaptability.
    • The approach provides a stable and accurate method for adaptive control applications.