Abstract
Aiming at the path tracking problem of intelligent commercial vehicles, based on the lateral driver model, the optimal preview control strategy is adopted. According to the relationship between the heading angle and the curvature of the path, the heading angle deviation feedback control is introduced. According to the relationship between the speed and the preview distance, a multi-point preview distance determination method with variable weight coefficient is proposed, which improves the effect of path tracking. In order to ensure the stability of path tracking, the model predictive control is used to restrict the wheel sideslip angle, which can improve the stability while ensuring the accuracy of the path tracking. Through co-simulation of TruckSim and Simulink, the optimal preview control and model predictive control are compared. The results show that the optimal preview control has better adaptability to vehicle load, but when the road adhesion coefficient is low, the vehicle will lose stability; while the model predictive control has better adaptability to vehicle load and road adhesion coefficient, and has better driving stability, has more accurate path tracking effect than the optimal preview control.
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Acknowledgements
This work was supported by National Natural Science Foundation (NNSF) of China (Grant No. 51207012).
This work was supported by Industrial Research Project of Science and Technology Department of Shanxi Province (Grant No. 2016GY-069).
This work was supported by Natural Science Foundation of Shanxi Province (Grant No. 2020JQ-385).
This work was supported by Special Fund for Basic Scientific Research of Central Colleagues, Chang’an University (Grant No. 300102228201).
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Li, Y., Wang, D., Feng, Q., Liu, Y., Nan, Y. (2022). Contrastive Study on Path Tracking Control Methods for Commercial Vehicles. In: Proceedings of China SAE Congress 2020: Selected Papers. Lecture Notes in Electrical Engineering, vol 769. Springer, Singapore. https://doi.org/10.1007/978-981-16-2090-4_44
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DOI: https://doi.org/10.1007/978-981-16-2090-4_44
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