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Updated: May 25, 2025

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Balance Control Method for Bipedal Wheel-Legged Robots Based on Friction Feedforward Linear Quadratic Regulator.

Aimin Zhang1, Renyi Zhou2, Tie Zhang3

  • 1GAC R&D Center, Guangzhou 511434, China.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel friction feedforward Linear Quadratic Regulator (LQR) control for wheel-legged robots, significantly improving balance and stability by compensating for motor friction. The new method enhances robot performance in challenging environments.

Keywords:
LQR controllerPSO algorithmStribeck friction modelbalance controlbipedal wheel-legged robots

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechatronics

Background:

  • Wheel-legged robots offer adaptable mobility in unstructured environments but face balance control challenges due to underactuation.
  • Hardware characteristics, like motor friction, negatively impact the dynamic convergence and stability of these robots.

Purpose of the Study:

  • To develop an advanced balance control method for wheel-legged robots that effectively addresses motor friction.
  • To enhance the stability, robustness, and convergence speed of wheel-legged robots during dynamic locomotion.

Main Methods:

  • A Linear Quadratic Regulator (LQR) controller was designed based on the robot's dynamics model.
  • A Stribeck friction model was identified using Particle Swarm Optimization (PSO) on data from a constant-speed excitation trajectory.
  • The identified friction model was integrated as feedforward compensation into the LQR controller.

Main Results:

  • Friction identification achieved a minimum standard deviation of approximately 0.30, with the model closely matching actual friction values.
  • The friction feedforward LQR algorithm demonstrated superior convergence performance compared to the baseline LQR.
  • Experimental results showed reduced oscillations, accelerated convergence, and improved stability and robustness across various terrains and disturbance scenarios.

Conclusions:

  • The proposed friction feedforward LQR balance control method effectively compensates for motor friction in wheel-legged robots.
  • This approach significantly enhances robot stability and dynamic performance, outperforming traditional LQR control.
  • The method offers a robust solution for improving the adaptability and reliability of wheel-legged robots in complex environments.