Intelligent vehicle trajectory tracking control algorithm based on recursive least square

LI Teng; REN Hongjuan

doi:10.12299/jsues.22-0209

Volume 37 Issue 4

Dec. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(4): 372-379, 408

LI Teng, REN Hongjuan. Intelligent vehicle trajectory tracking control algorithm based on recursive least square[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209

Citation:

LI Teng, REN Hongjuan. Intelligent vehicle trajectory tracking control algorithm based on recursive least square[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209

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Intelligent vehicle trajectory tracking control algorithm based on recursive least square

doi: 10.12299/jsues.22-0209

LI Teng,
REN Hongjuan^,

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2022-07-09
Publish Date: 2023-12-30

Abstract

Abstract

A novel adaptive lateral optimal tracking control algorithm for real-time estimation of tire lateral stiffness was proposed. The approximate linear range of tire lateral forces were altered with variation in road surface adhesion coefficients, rendering tire lateral stiffness estimations based on linear approximations unreliable. Utilizing the recursive least squares algorithm and taking tire slip angle and lateral force as inputs, the tire's lateral stiffness was estimated in real time and an adaptive linear quadratic regulator (ALQR) controller was developed. The effectiveness and robustness of the algorithm were validated on a joint simulation platform combining Matlab/Simulink and Carsim. The results demonstrated that under various road surface adhesion conditions and at different vehicle speeds, the performance of the designed control algorithm consistently surpassed that of the traditional linear quadratic regulator (LQR) control algorithm. Specifically, the maximum lateral position error and yaw angle error were reduced by 81.5% and 73.0%, respectively. Real-vehicle tests empirically validated the practical applicability and effectiveness of the algorithm, with the maximum trajectory tracking error being only 0.56 m.
- recursive least squares,
- track tracking,
- optimum control,
- adaptive control

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References(25)

References

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