An Adaptive Model Predictive Control Strategy for Path Following of Autonomous Vehicles Based on Tire Cornering Stiffness Estimation

被引:2
|
作者
Zhang, Yuhang [1 ,2 ]
Wang, Weida [1 ,2 ]
Yang, Chao [1 ]
Ma, Mingyue [3 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Chongqing Innovat Ctr, Chongqing 401120, Peoples R China
[3] Minist Publ Secur Peoples Republ China, Rd Traff Safety Res Ctr, Beijing 100062, Peoples R China
基金
中国国家自然科学基金;
关键词
Autonomous Vehicles; Path Following; Adaptive Control; Recursive Least Square; Model Predictive Control; DISTURBANCE; TRACKING;
D O I
10.1109/CCDC52312.2021.9601550
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Path following performance is a crucial issue for autonomous vehicles. Due to the influence of parameter uncertainties, inevitable deviation occurs during path following process. To solve this problem, an adaptive model predictive control strategy based on tire cornering stiffness estimation is proposed for path following. Firstly, the recursive-least-square (RLS) method is applied to estimate the uncertainty of tire cornering stiffness in real time. Secondly, based on the real-time update system model, a model predictive control (MPC) scheme is proposed to achieve path following. In this way, the proposed strategy can adapt to the changes of driving conditions. Finally, lane change maneuver is performed in simulation to verify the effectiveness of the proposed strategy. The results show that the lateral offset using the proposed strategy is reduced by 10% compared with the traditional MPC.
引用
收藏
页码:1904 / 1909
页数:6
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