Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for...

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Autonomous vehicles require robust path-tracking for safety due to dynamic driving conditions.
• Traditional control methods struggle with real-time uncertainties in vehicle dynamics and environment.
• Sliding mode control (SMC) offers robustness but faces challenges with unknown uncertainties.

Purpose of the Study:

• To develop an adaptive and robust path-tracking algorithm for autonomous vehicles.
• To address the limitations of existing controllers in handling unknown uncertainties.
• To integrate adaptive feedback control (AFC) with SMC for enhanced stability and performance.

Main Methods:

• Sliding mode control (SMC) was employed to manage switching gain based on the sliding surface.
• An adaptive feedback control (AFC) component was integrated to adjust for bounded uncertainties.
• The controller design focused on achieving zero control error despite unknown system variations.

Main Results:

• The integrated AFC-SMC controller demonstrated superior control performance in autonomous driving scenarios.
• The proposed method effectively adjusted for bounded uncertainties, ensuring closed-loop stability.
• Illustrative examples confirmed the controller's robustness and adaptive capabilities.

Conclusions:

• The integrated adaptive feedback control and sliding mode control method provides a robust solution for autonomous vehicle path tracking.
• This approach enhances stability and performance by effectively managing unknown uncertainties.
• The developed controller is expected to see wide adoption in autonomous driving systems.