REINFORCEMENT LEARNING-BASED ADAPTIVE MOTION CONTROL FOR AUTONOMOUS VEHICLES VIA ACTOR-CRITIC STRUCTURE

被引：1

作者：

Wang, Honghai ^{[1
]}

Wei, Liangfen ^{[1
]}

Wang, Xianchao ^{[1
]}

He, Shuping ^{[2
]}

机构：

[1] Chaohu Univ, Sch Comp & Artificial Intelligence, Hefei 238024, Peoples R China

[2] Anhui Univ, Sch Elect Engn & Automat, Hefei 230601, Peoples R China

来源：

DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES S | 2024年 / 17卷 / 09期

关键词：

Autonomous vehicle systems; neural network; path tracking; reinforce- ment learning; PATH-TRACKING; SYSTEMS;

D O I：

10.3934/dcdss.2024021

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, an optimized adaptive human-machine collaborative torque control methodology is developed for a class of autonomous vehicle systems with unknown disturbances by employing a reinforcement learning technology with identifier-critic-actor architecture. The criticism method is proposed to assess the performance of the control and to give feedback to the actor. Furthermore, a human-vehicle-road model with steering torque as input is constructed to describe the vehicle's dynamic response and the driver's maneuvering behavior. The stability of the control system is analyzed by means of the Lyapunov theory. Finally, the effectiveness of the control methodology is illustrated by means of a numerical example.

引用

页码：2894 / 2911

页数：18

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