Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC
Wei Tan1, Mengfei Wang1, Ke Ma1
1Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China.
Sensors (Basel, Switzerland)
|April 13, 2024
Summary
This study introduces an improved adaptive model predictive control (AMPC) method for intelligent vehicle trajectory tracking. The new approach enhances accuracy and stability in complex driving conditions, offering better adaptability and robustness.
Area of Science:
- Automotive Engineering
- Control Systems
- Robotics
Background:
- Intelligent vehicle trajectory tracking faces challenges with adaptability, accuracy, and robustness in complex environments.
- Uncertain road conditions and dynamic driving scenarios limit current tracking system performance.
Purpose of the Study:
- To develop an improved adaptive model predictive control (AMPC) method for enhanced intelligent vehicle trajectory tracking.
- To increase tracking accuracy and driving stability under uncertain and complex road conditions.
Main Methods:
- Utilized an unscented Kalman filter to estimate tire lateral forces in real-time.
- Designed an adaptive estimation strategy for tire cornering stiffness.
- Developed a dynamic prediction time-domain adaptive model optimized for vehicle speed and road adhesion.
Main Results:
- Achieved low lateral position and heading angle errors in trajectory tracking simulations.
- Demonstrated high trajectory-tracking control accuracy across various driving and road adhesion conditions.
- Verified the effectiveness of the proposed AMPC technique through co-simulation.
Conclusions:
- The improved AMPC method significantly enhances intelligent vehicle trajectory tracking performance.
- This approach offers better adaptability and robustness for intelligent vehicle control systems.
- Provides a valuable reference for optimizing intelligent vehicle tracking control systems.
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