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Spatial hybrid adaptive impedance learning control for robots in repetitive interactive tasks.

Jiantao Yang1, Tairen Sun1, Hongjun Yang2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

ISA Transactions
|February 24, 2023
PubMed
Summary

This study introduces a spatial hybrid adaptive impedance learning control (SHAILC) strategy for physical human-robot interaction (PHRI). It enables robots to adapt to human impedance in repetitive tasks with varying completion times.

Keywords:
Adaptive controlHybrid adaptationRepetitive taskRobot control

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Area of Science:

  • Robotics
  • Control Systems
  • Human-Robot Interaction

Background:

  • Existing model-based impedance learning control methods for physical human-robot interaction (PHRI) in repetitive tasks are limited to constant task completion times.
  • This restriction hinders the application of these methods in scenarios with variable task durations.

Purpose of the Study:

  • To propose a novel spatial hybrid adaptive impedance learning control (SHAILC) strategy for PHRI in repetitive tasks with different completion times.
  • To address the limitations of existing methods by incorporating spatial periodic characteristics.

Main Methods:

  • The SHAILC strategy utilizes spatial periodic adaptation to estimate time-varying human impedance.
  • Differential adaptation is employed to estimate unknown constant robotic parameters.
  • Deadzone modifications are incorporated to maintain parameter estimation accuracy with small tracking errors.

Main Results:

  • The control stability was rigorously analyzed using a Lyapunov-based approach in the spatial domain.
  • The effectiveness and superiority of the SHAILC strategy were demonstrated on a parallel robot.
  • The system successfully handled repetitive tasks with varying completion times.

Conclusions:

  • The proposed SHAILC strategy effectively enables variable impedance regulation for PHRI in repetitive tasks with different completion times.
  • This advancement expands the applicability of impedance learning control in dynamic human-robot collaboration scenarios.
  • The method offers robust performance and stability in complex interaction tasks.