Multi-model adaptive predictive control for path following of autonomous vehicles

被引:14
作者
Liang, Yixiao [1 ,2 ]
Li, Yinong [1 ]
Khajepour, Amir [2 ]
Zheng, Ling [1 ]
机构
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[2] Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON, Canada
基金
中国国家自然科学基金;
关键词
road vehicles; nonlinear control systems; stability; adhesion; vehicle dynamics; adaptive control; predictive control; time-varying systems; tyres; Lyapunov methods; robust control; autonomous vehicles; tire cornering stiffness; low adhesive conditions; novel multimodel adaptive predictive control; model predictive path; road-related kinematics relationship; model uncertainties; multiple model adaptive theory; recursive least adaptive law; multiple-model adaptive law; model predictive control; MODEL; FRAMEWORK;
D O I
10.1049/iet-its.2020.0357
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The uncertainties in tire cornering stiffness can degrade the path following the performance of autonomous vehicles, especially in low adhesive conditions, to deal with this problem, a novel multi-model adaptive predictive control is proposed in this study. Firstly, a model predictive path following controller is designed based on a combined model of vehicle dynamics and road-related kinematics relationship. Then, to deal with the model uncertainties, the multiple model adaptive theory is introduced, and the recursive least adaptive law is proposed with its convergence proved by Lyapunov theory. Finally, the multiple-model adaptive law is combined with the proposed model predictive control by a convex polytope of tire cornering stiffness. In this way, the proposed algorithm can be adaptive to the uncertainties of tire cornering stiffness. Simulation results show the effectiveness and robustness of the proposed method to the uncertainties of the tire cornering stiffness resulting in an excellent performance in any road condition without introducing conservativeness.
引用
收藏
页码:2092 / 2101
页数:10
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