Approximation-Based Robust Adaptive Automatic Train Control: An Approach for Actuator Saturation

被引：149

作者：

Gao, Shigen ^{[1
,2
]}

Dong, Hairong ^{[1
]}

Chen, Yao ^{[3
]}

Ning, Bin ^{[1
]}

Chen, Guanrong ^{[4
]}

Yang, Xiaoxia ^{[1
,2
]}

机构：

[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China

[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

[3] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

[4] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2013年 / 14卷 / 04期

关键词：

Actuator saturation; automatic train operation (ATO); neural network; robust adaptive control; OUTPUT-FEEDBACK; SYSTEMS; STABILIZATION; MODEL;

D O I：

10.1109/TITS.2013.2266255

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper addresses an on-line approximation-based robust adaptive control problem for the automatic train operation (ATO) system under actuator saturation caused by constraints from serving motors. A robust adaptive control law is proposed, which is proved capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system. To cope with actuator saturation, another robust adaptive control is proposed for the ATO system, by explicitly considering the actuator saturation nonlinearity other than unknown system parameters, which is also proved capable of stabilizing the closed-loop system. Simulation results are presented to verify the effectiveness of the two proposed control laws.

引用

页码：1733 / 1742

页数：10

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