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Safety-critical data-enabled predictive control for wheeled mobile robot.

Ali Can Erüst1, Fatma Yıldız Taşcıkaraoğlu1, İbrahim Beklan Küçükdemiral2

  • 1Department of Electrical and Electronics Engineering, Mugla Sitki Kocman University, Mugla, Türkiye.

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Summary
This summary is machine-generated.

This study enhances robot navigation using data-enabled predictive control (DeePC) for unknown systems. Adaptive regularization improved trajectory tracking accuracy by 20.2% in complex environments.

Keywords:
Adaptive regularizationData-enabled predictive controlDiscrete time control barrier functionsWheeled mobile robot

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Mobile robots require robust control for safety-critical tasks like obstacle avoidance.
  • Data-enabled predictive control (DeePC) offers a model-free approach for linear systems.
  • Adapting DeePC for nonlinear systems and ensuring safety are significant challenges.

Purpose of the Study:

  • To develop a safety-critical planning framework for wheeled mobile robots with unknown dynamics.
  • To enhance the robustness and accuracy of data-enabled predictive control for nonlinear systems.
  • To guarantee system stability and bounded trajectory tracking in complex environments.

Main Methods:

  • Applied local linear approximation with online data updates to adapt DeePC for nonlinear systems.
  • Integrated an adaptive Lasso-based regularization term into the DeePC cost function for improved robustness.
  • Incorporated discrete-time control barrier functions within a quadratic programming safety filter for enhanced safety.

Main Results:

  • Adaptive regularization reduced trajectory tracking error by approximately 20.2% in simulations.
  • The proposed framework demonstrated practical stability and bounded trajectory tracking.
  • Real-time simulations in ROS-Gazebo validated the effectiveness of the approach.

Conclusions:

  • The developed DeePC framework with adaptive regularization and safety filtering is effective for real-world robot navigation.
  • This approach enhances safety and performance for robots operating in unknown, complex environments.
  • The method provides a promising solution for safety-critical control applications in robotics.