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Efficient Online Controller Tuning for Omnidirectional Mobile Robots Using a Multivariate-Multitarget Polynomial

Alam Gabriel Rojas-López1, Miguel Gabriel Villarreal-Cervantes1, Alejandro Rodríguez-Molina2

  • 1Mechatronics Section, Postgraduate Department, OMD Laboratory, Instituto Politécnico Nacional-Centro de Innovación y Desarrollo Tecnológico en Cómputo, Mexico City 07700, Mexico.

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Summary
This summary is machine-generated.

This study introduces an efficient online bioinspired controller tuning method for mobile robots using surrogate modeling. The approach significantly reduces computational load while maintaining or improving controller performance in uncertain conditions.

Keywords:
evolutionary optimizationomnidirectional mobile robotonline controller tuningpolynomial response surface method

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Computational Intelligence

Background:

  • Mobile robots operate in unpredictable environments, necessitating advanced controllers for dynamic parameter adjustment.
  • Online bioinspired controller tuning offers innovative solutions for handling uncertainties but demands substantial computational resources.

Purpose of the Study:

  • To develop an efficient online bioinspired controller tuning method for omnidirectional mobile robots using surrogate modeling.
  • To reduce the computational load of controller tuning without compromising performance in dynamic environments.

Main Methods:

  • Incorporation of the polynomial response surface method for system identification and behavior prediction.
  • Development of an online bioinspired controller tuning approach leveraging surrogate modeling.
  • Comparative analysis against state-of-the-art online, offline robust, and offline non-robust bioinspired tuning methods.

Main Results:

  • The proposed method reduces computational load by up to 62.85% compared to existing online approaches.
  • Controller performance is maintained under adverse conditions and improved by up to 93% compared to offline methods.
  • The approach demonstrates superior performance and reduced computational load against a Gaussian process regression-based surrogate tuning method.

Conclusions:

  • The developed controller tuning approach significantly decreases execution time for mobile robot control systems.
  • It maintains closed-loop performance even under adverse environmental uncertainties and disturbances.
  • This represents the first application of such a surrogate-assisted controller tuning strategy on an omnidirectional mobile robot.