Longitudinal Velocity Tracking Control of a 4WID Electric Vehicle

被引：14
|
作者：
Hang, Peng [1 ]
Chen, Xinbo [2 ]
Zhang, Bang [3 ]
Tang, Tingju [1 ]
机构：
[1] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China
[2] Tongji Univ, Clean Energy Automot Engn Ctr, Shanghai 201804, Peoples R China
[3] Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China
来源：
IFAC PAPERSONLINE | 2018年 / 51卷 / 31期
基金：
中国国家自然科学基金;
关键词：
Longitudinal velocity tracking; sliding model control; RBF neural network; four-wheel independent drive; electric vehicle;
D O I：
10.1016/j.ifacol.2018.10.129
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel four-wheel independent drive (4WID) electric vehicle (EV) is proposed as an autonomous ground vehicle (AGV) in this paper. Since longitudinal motion control is the fundamental problem of autonomous vehicle control, a traditional sliding mode control (SMC) algorithm and an improved adaptive sliding mode control (ASMC) algorithm using Radial Basis Function (RBF) neural network are applied to longitudinal velocity tracking controller design. To evaluate the performance of the designed controllers for the 4WID EV, two simulation maneuvers are carried out including straight line condition and single lane-change condition. The simulation results indicate that the longitudinal velocity tracking controller using ASMC has smaller tracking error and better disturbance rejection performance than SMC. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：790 / 795
页数：6
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