Path Preview Tracking for Autonomous Vehicles Based on Model Predictive Control

Lu, Yanrong; Zhu, Jia; Wang, Zhiwen; Zhang, Jindou

doi:10.1109/icus55513.2022.9987142

Cited by 1 publication

(1 citation statement)

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“…Therefore, this paper uses the ratio of the estimated tire lateral force to the linear tire force to correct the slip stiffness. As a result, the proportion factor of tire slip stiffness is expressed as: (31) Therefore, the corrected tire lateral deflection stiffness is expressed as:…”

Section: Guidelines For Adjusting Lateral Deflection Stiffnessmentioning

confidence: 99%

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

Yang,

Qian,

et al. 2024

WEVJ

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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.

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Section: Guidelines For Adjusting Lateral Deflection Stiffnessmentioning

confidence: 99%