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Fuzzy adaptive nonlinear MIMO control for rigid coupled multibody robots using reinforcement learning model.

Chenxu Duan1, Luwen Wang2, Shuangcen Li3

  • 1School of Intelligent Manufacturing, Sichuan University Jinjiang College, Meishan, 620860, Sichuan, China. chasel_duan@163.com.

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Summary

This study introduces a novel adaptive MIMO control for robots using reinforcement learning and the starfish optimization algorithm (SFOA). It enhances robotic movement stability and flexibility against disturbances for precise trajectory tracking.

Keywords:
Adaptive MIMO controlFuzzy reinforcement learning (FRL)Multi-degree-of-freedom robotsNonlinear dynamics adaptationStar fish optimization algorithm (SFOA)

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Mechatronic systems like robots have multiple degrees of freedom (DOF), crucial for industry and daily life.
  • Accurate characterization of these systems is challenging due to coupling, unknown dynamics, and external perturbations, limiting traditional model-based control.
  • Existing control methods struggle with the complexities of real-world robotic applications.

Purpose of the Study:

  • To develop an adaptive MIMO (Multiple-Input Multiple-Output) control approach for robots and robotic arms.
  • To enhance the stability, flexibility, and real-time performance of robotic systems facing environmental uncertainties and disturbances.
  • To achieve quick and precise trajectory tracking in complex robotic systems.

Main Methods:

  • A reinforcement learning-based adaptive MIMO control strategy was developed.
  • Integration of the starfish optimization algorithm (SFOA), fuzzy reinforcement learning, and finite-time convergence principles.
  • Utilized joint space modeling for governing equations and quaternion modeling for control strategy implementation.
  • Dynamic adaptation, real-time learning, and instantaneous feedback were incorporated into a multivariate feedback architecture.

Main Results:

  • The proposed control strategy demonstrated outstanding performance in simulations.
  • The approach enhanced stability and flexibility of robot movements under disturbances.
  • Achieved quick and precise trajectory tracking, showcasing improved real-time performance, accuracy, and robustness.
  • Successfully tested on robots with two and five degrees of freedom.

Conclusions:

  • The reinforcement learning-based adaptive MIMO control effectively addresses challenges in robotic system characterization and control.
  • The integration of SFOA, fuzzy reinforcement learning, and finite-time convergence offers a robust solution for complex robotic tasks.
  • The developed control strategy provides superior real-time performance, accuracy, and robustness, making it suitable for critical applications.