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Related Experiment Video

Updated: Aug 16, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Adaptive Interaction Control of Compliant Robots Using Impedance Learning.

Tairen Sun1, Jiantao Yang1

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

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|December 23, 2022
PubMed
Summary

This study introduces an adaptive control for compliant robots using series elastic actuators (SEAs). It estimates environmental impedance and robot uncertainties without measuring interaction forces, ensuring stable robot movement.

Keywords:
adaptive controlcompliant robotimpedance controlimpedance learning

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Series elastic actuators (SEAs) enhance robot compliance but require accurate force sensing for control.
  • Existing control methods often rely on direct measurement of robot-environment interaction forces, which can be challenging or impossible.
  • Adaptive control strategies are crucial for handling uncertainties in robotic systems.

Purpose of the Study:

  • To develop an impedance learning-based adaptive control strategy for SEA-driven compliant robots.
  • To enable control without direct measurement of robot-environment interaction forces.
  • To estimate environmental impedance and robot parameter uncertainties online.

Main Methods:

  • Utilizing a command filter-based adaptive backstepping approach for controller design.
  • Implementing adaptive learning laws to estimate environmental impedance profiles and robotic parameter uncertainties.
  • Employing Lyapunov-based theoretical analysis to guarantee stability and boundedness of errors.

Main Results:

  • The proposed controller effectively manages robot dynamics and uncertainties without force sensing.
  • Tracking errors and estimation errors are proven to be semiglobally uniformly ultimately bounded.
  • Simulations demonstrate the control effectiveness on a compliant robot arm.

Conclusions:

  • The impedance learning-based adaptive control strategy offers a viable solution for controlling SEA-driven robots in uncertain environments.
  • The method successfully eliminates the need for direct interaction force measurement, simplifying implementation.
  • The theoretical analysis confirms the robustness and stability of the proposed control approach.