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Practical issues in data-driven model-free adaptive control for an omnidirectional mobile manipulator.

Chao Ren1, Jingyi Zhang1, Liang Hu2

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.

ISA Transactions
|August 4, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances model-free adaptive control (MFAC) for mobile robots, introducing solutions for platform rotation, initial value estimation, and trajectory convergence. Experimental verification on an omnidirectional mobile manipulator demonstrates improved control performance.

Keywords:
Data-drivenModel-free adaptive controlOmnidirectional mobile robotTrajectory tracking control

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Model-free adaptive control (MFAC) is a data-driven approach for robotic systems.
  • Existing MFAC schemes face challenges with mobile robots, particularly omnidirectional mobile manipulators (OMMs) involving platform rotation.
  • The assumption of constant pseudo-partial derivative (PPD) sign and unclear initial value settings hinder MFAC application.

Purpose of the Study:

  • To address practical limitations of MFAC when applied to mobile robots with rotation.
  • To propose novel solutions for PPD sign constancy, initial value estimation, and trajectory convergence in MFAC.
  • To experimentally validate an enhanced MFAC scheme on a prototype OMM.

Main Methods:

  • Introduced a new coordinate frame to handle platform rotation in MFAC.
  • Developed an initial value setting method for PPD estimation with physical interpretation.
  • Proposed a data-driven MFAC controller integrating sliding mode control for improved convergence.

Main Results:

  • Successfully applied MFAC to mobile robots with rotation using the novel coordinate frame.
  • The proposed initial value setting method enhances control system stability.
  • The integrated MFAC and sliding mode control demonstrated effective trajectory convergence on an OMM.

Conclusions:

  • The developed MFAC scheme effectively addresses key practical issues in mobile robot control.
  • This work presents the first experimentally verified MFAC scheme for a rotating mobile robot prototype.
  • The findings pave the way for more robust and adaptable data-driven control in robotics.