Dual heuristic programming vehicle path tracking control via receding horizon
GUO Hong-yan
LI Guang-yao
LIU Jun
GUO Jing-zheng
TAN Zhong-qiu
LÜ Ying
Abstract：To improve the path tracking accuracy of intelligent vehicles and reduce the impact of vehicle model un-certainty under high speed and large curvature scenarios,this paper proposes an intelligent vehicle path tracking control strategy based on receding horizon dual heuristic programming(RHDHP).Firstly,a vehicle system model,capable of char-acterizing non linear characteristics of lateral tire forces,is established by combining with the magic formula.Subsequently,an optimal control method using dual heuristic programming(DHP)under the framework of receding horizon optimization is designed.The DHP structure in this method ensures an approximate optimal solution for the vehicle under nonlinear characteristics,while the introduction of receding horizon optimization enhances the adaptability of the vehicle system to environmental changes.Additionally,the convergence of the RHDHP method and the stability of the closed loop system are theoretically analyzed.Finally,the effectiveness of the proposed method is verified through simulations.
Keywords：vehicle path trackingdual heuristic programmingmodel predictive controlreinforcement learning
Publication Date：2025-09-30
Online Publishing Date：2025-10-28(First online date of this platform, not the publication date of the document)
Pages：11( 1746-1756 )