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Combined control algorithm based on synchronous reinforcement learning for a self-balancing bicycle robot.

Lei Guo1, Hongyu Lin1, Jiale Jiang1

  • 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

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|November 25, 2023
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Summary
This summary is machine-generated.

This study presents a novel control strategy for balancing a bicycle robot using feedback linearization and actor-critic neural networks. The method ensures stable robot motion and optimal control policies, validated by simulations and experiments.

Keywords:
Bicycle robotCombined control algorithmDynamics modelNonaffine nonlinear systemSynchronous reinforcement learning

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Bicycle robot balance control is challenging due to inherent nonlinear dynamics.
  • Existing methods often rely on mechanical regulators or external support.
  • Autonomous rectilinear motion requires robust balance maintenance.

Purpose of the Study:

  • To develop a balance control strategy for a bicycle robot without external stabilizers.
  • To establish a model for the robot's nonaffine nonlinear dynamics.
  • To ensure stable and optimal control during rectilinear movement.

Main Methods:

  • Developed an input nonaffine nonlinear dynamics model based on moment balance.
  • Transformed the system into an affine nonlinear system using equivalent control.
  • Designed a feedback linearization controller and a synchronous policy iteration algorithm.
  • Utilized actor-critic neural networks (NNs) for control law implementation.
  • Proposed weight tuning laws for NNs, ensuring stability via Lyapunov analysis.

Main Results:

  • Achieved balance control for a bicycle robot in approximate rectilinear motion.
  • Demonstrated the transformation of nonaffine to affine nonlinear systems.
  • Guaranteed closed-loop stability and NN weight convergence.
  • Verified the optimality of the control policy.
  • Validated the algorithm's effectiveness through simulations and real-world experiments.

Conclusions:

  • The proposed actor-critic based feedback linearization controller effectively manages bicycle robot balance.
  • The method ensures system stability and optimal control policy.
  • The approach is validated and applicable to real-world robotic systems.