期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 19, 期 10, 页码 3121-3132出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2017.2772306
关键词
String stability; actuator saturation; constant time headway (CTH) policy; Chebyshev neural network (CNN); sliding mode
资金
- National Natural Science Foundation of China [61773056, 61403279, 61673055, 61673056, 61603274]
- China Postdoctoral Science Foundation [2017M610046]
- Fundamental Research Funds for the Central Universities of USTB [230201606500061, FRF-BD-16-005A]
- Beijing Key Discipline Development Program [XK100080537]
- National University of Singapore [RCA-14/123]
A neural network-based distributed adaptive approach combined with sliding mode technique is proposed for vehicle-following platoons in the presence of input saturation, unknown unmodeled nonlinear dynamics, and external disturbances. A simple and straightforward strategy by adjusting only a single parameter is proposed to compensate for the effect of input saturation. Two spacing polices (i.e., traditional constant time headway policy and modified constant time headway policy) are used to guarantee string stability and maintain the desired spacing. Chebyshev neural networks (CNN) are used to approximate the unknown nonlinear functions in the followers online, and the implementation of the basic functions of CNN depends only on the leader's velocity and acceleration. Furthermore, unlike existing approaches, the nonlinearities of consecutive vehicles need not satisfy the matching condition. Finally, simulations are carried out to illustrate the effectiveness and the advantage of the proposed methods, first using a numerical example, followed by a practical example of a high speed train platoon.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据