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Modelling and robust controller design for an underactuated self-balancing robot with uncertain parameter estimation.

Osama A Choudhry1, Muhammad Wasim2, Ahsan Ali3

  • 1Department of Physics and Electrical Engineering, Universität Bremen, Bremen, Germany.

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Summary
This summary is machine-generated.

This study introduces an enhanced control method for self-balancing robots (SBRs) that accounts for payload variations. The novel approach improves stability and performance, especially in indoor applications.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechatronics

Background:

  • Self-balancing robots (SBRs) face stability challenges due to unaddressed payload variations.
  • Existing control techniques often assume nominal rider mass and height, limiting performance.
  • A robust mathematical model is crucial for effective SBR control with dynamic payloads.

Approach:

  • Developed a robust control strategy using an extended Kalman filter (EKF)-based sliding mode controller (SMC).
  • Integrated an extensive mathematical model of the SBR and payload dynamics.
  • Estimated rider mass and height using EKF to enhance SBR control.

Key Points:

  • The proposed EKF-based SMC method effectively handles unknown payload parameters and variations.
  • Achieved zero steady-state error and no overshoot in simulation results.
  • Improved conventional SMC performance and resolved stability issues in the reaching phase.

Conclusions:

  • The novel control approach significantly enhances SBR performance and stability under varying payloads.
  • This method is well-suited for indoor SBR applications requiring high precision.
  • Provides a comprehensive comparison of control techniques for SBRs.