Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

• Published in: World electric vehicle journal Vol. 15; no. 3; p. 95
• Main Authors: Yang, Shaobo, Qian, Yubin, Hu, Wenhao, Xu, Jiejie, Sun, Hongtao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2024
• Subjects: Adaptive control; adaptive model prediction; Algorithms; Automatic control; automatic vehicles; Computer peripherals; Control algorithms; Control systems; Controllers; Deflection; Error correction; Kalman filters; Kinematics; minimum new interest covariance; Neural networks; Path tracking; path-tracking control; Predictive control; Road conditions; Roads & highways; square root volume Kalman filter; Stiffness; Tire force; Tires; Tracking control; Tracking errors; Trajectory control; Vehicles
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj15030095

For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems.