Open AccessMulti‐model adaptive predictive control for path following of autonomous vehicles
Author(s) -
Liang Yixiao,
Li Yig,
Khajepour Amir,
Zheng Ling
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0357
Subject(s) - control theory (sociology) , model predictive control , robustness (evolution) , kinematics , stiffness , adaptive control , lyapunov function , computer science , vehicle dynamics , engineering , control (management) , automotive engineering , artificial intelligence , biochemistry , chemistry , physics , structural engineering , classical mechanics , nonlinear system , quantum mechanics , gene
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.