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Stable Gaussian process tracking control of antagonistic variable stiffness actuators.

Zhigang Zou1,2, Wei Li3, Chenguang Yang4

  • 1College of Mechatronics Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.

Scientific Reports
|June 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Gaussian process regression controller for variable stiffness actuators (VSAs). The method enhances tracking accuracy and generalization for agonistic-antagonistic (AA) VSAs with unknown dynamics.

Keywords:
Compliant actuationGaussian processMachine learning for controlMotion controlVariable stiffness actuator

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Area of Science:

  • Robotics
  • Control Systems
  • Machine Learning

Background:

  • Variable stiffness actuators (VSAs) present significant control challenges due to nonlinear dynamics.
  • Traditional control methods often compromise VSA compliance or lack generalization capabilities.

Purpose of the Study:

  • To develop a stable tracking controller for agonistic-antagonistic (AA) VSAs using Gaussian process (GP) regression.
  • To address unknown dynamics and improve both tracking accuracy and generalization.

Main Methods:

  • Gaussian process (GP) regression was employed to learn the inverse dynamics of AA-VSAs.
  • A stable tracking controller combining feedforward and low-gain feedback was designed.
  • Model fidelity was enhanced using GP's predicted variance.

Main Results:

  • The controller demonstrated uniformly ultimately bounded tracking error.
  • Tracking error was shown to exponentially converge to a small ball under a given probability.
  • Experimental validation on the qbmove Advanced AA-VSA confirmed superior tracking accuracy and generalization.

Conclusions:

  • The proposed GP-based controller effectively manages unknown dynamics in AA-VSAs.
  • This approach preserves the inherent compliance of VSAs while achieving high performance.
  • The method offers a promising solution for advanced robotic control applications.