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

© The Institution of Engineering and Technology
Received 03/06/2020, Accepted 27/01/2021, Revised 28/12/2020, Published 01/02/2021

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.

Inspec keywords: Lyapunov methods; road vehicles; adhesion; vehicle dynamics; stability; predictive control; tyres; robust control; nonlinear control systems; time-varying systems; adaptive control

Other keywords: recursive least adaptive law; autonomous vehicles; tire cornering stiffness; multiple-model adaptive law; novel multimodel adaptive predictive control; model predictive control; low adhesive conditions; multiple model adaptive theory; model uncertainties; vehicle dynamics; model predictive path; road-related kinematics relationship

Subjects: Self-adjusting control systems; Nonlinear control systems; Vehicle mechanics; Stability in control theory; Optimal control

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