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Adaptive robust controller using intelligent uncertainty observer for mechanical systems under non-holonomic

Xiaolong Chen1, Wenyu Liang2, Han Zhao3

  • 1School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui Province 230009, China; Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117582, Singapore; Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore.

ISA Transactions
|May 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive robust controller for mechanical systems with non-holonomic trajectories and uncertainties. The novel controller unifies trajectory handling and enhances system robustness against unknown factors.

Keywords:
Closed-form solutionFuzzy cerebellar model articulation controller neural networkNon-holonomic reference trajectoryUdwadia controllerUncertainty

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Area of Science:

  • Robotics and Control Systems
  • Mechanical Engineering
  • Artificial Intelligence

Background:

  • Mechanical systems often face challenges with non-holonomic trajectories and uncertainties, impacting control performance.
  • Existing control strategies may not effectively address both holonomic and non-holonomic references simultaneously.
  • System uncertainties can lead to performance degradation if not properly managed.

Purpose of the Study:

  • To develop a novel explicit adaptive robust controller for mechanical systems with non-holonomic reference trajectories and uncertainties.
  • To unify the handling of holonomic and non-holonomic reference trajectories within a single control framework.
  • To enhance system robustness and compensate for uncertainties and residual errors.

Main Methods:

  • Employed the Udwadia controller structure for unified trajectory handling.
  • Designed an observer using a fuzzy cerebellar model articulation controller neural network to identify uncertainties.
  • Incorporated a robust term for initial deviation restraint and a compensatory term for observer-induced errors.
  • Verified controller stability using Lyapunov stability analysis.

Main Results:

  • The proposed controller effectively handles both holonomic and non-holonomic reference trajectories.
  • The uncertainty observer successfully identified system uncertainties, mitigating performance degradation.
  • The robust and compensatory terms enhanced system stability and accuracy.
  • Lyapunov stability analysis confirmed the theoretical soundness of the controller.

Conclusions:

  • The developed explicit adaptive robust controller offers a unified and robust solution for mechanical systems with complex trajectories and uncertainties.
  • The integration of a fuzzy cerebellar model articulation controller neural network observer provides effective uncertainty identification and compensation.
  • The controller demonstrates significant potential for improving the performance and reliability of mechanical systems in challenging operational conditions.