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Novel hybrid adaptive controller for manipulation in complex perturbation environments.

Alex M C Smith1, Chenguang Yang2, Hongbin Ma3

  • 1Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK.

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Summary

This study introduces a novel hybrid adaptive controller for robot manipulators, improving performance by minimizing control effort and tracking error. It effectively handles external disturbances and uses fuzzy logic for adaptive learning, reducing manual tuning.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Robot control often involves trade-offs between task-space and joint-space approaches.
  • External disturbances and end-effector perturbations challenge manipulator control accuracy.
  • Adaptive control methods can improve performance but often require extensive tuning.

Purpose of the Study:

  • To develop and evaluate a novel hybrid adaptive control scheme for robot manipulators.
  • To minimize control effort and tracking error in the presence of external disturbances.
  • To introduce a meta-learning mechanism for online adaptation of control parameters.

Main Methods:

  • A hybrid control scheme combining task-space and joint-space control strategies was designed.
  • The controller incorporates a human-like adaptive design to minimize errors and effort.
  • Extensive simulations were conducted using a Baxter robot manipulator subjected to environmental interactions and end-effector perturbations.
  • A novel fuzzy control-based method for online adaptation of learning parameters (meta-learning) was implemented.

Main Results:

  • The proposed hybrid adaptive controller demonstrated superior performance compared to individual task-space and joint-space controllers.
  • The controller effectively managed external disturbances and end-effector perturbations.
  • The meta-learning mechanism significantly improved performance and eliminated the need for manual parameter tuning through trial testing.

Conclusions:

  • The novel hybrid adaptive controller offers significant advantages for robot manipulator control, particularly in dynamic and uncertain environments.
  • The integration of fuzzy logic for meta-learning provides an efficient and effective approach to online parameter adaptation.
  • This approach enhances robot performance and reduces development time by minimizing manual tuning requirements.