Data-Driven Lateral Fault-tolerance Control of Autonomous Vehicle System Using Reinforcement Learning

被引：0
|
作者：
Li, Yan [1 ]
Zhang, Hao [1 ]
Wang, Zhuping [1 ]
机构：
[1] Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China
来源：
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA) | 2020年
基金：
中国国家自然科学基金;
关键词：
ADAPTIVE OPTIMAL-CONTROL; CONTINUOUS-TIME SYSTEMS; PATH-FOLLOWING CONTROL; TRACKING CONTROL; STABILIZATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a data driven lateral fault-tolerant control (LFTC) method is proposed for four wheels drive vehicles, which considering both vehicle velocity and trajectory tracking performance. The novel design of the lateral control employs with zero-sum game and adaptive dynamics programming technique to solve the Riccati equation without requiring the knowledge of system, only using online data. The LFTC consists of the data driven off-policy and adaptive control. The adaptive parameters adjusted online to compensate the actuator faults automatically. The tracking system is asymptotically stable with the disturbance attenuation level gamma. Finally, simulation is provided to show the effectiveness of the proposed method.
引用
收藏
页码：1410 / 1415
页数：6
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