Adaptive Neural-Fuzzy Robust Position Control Scheme for Maglev Train Systems With Experimental Verification

被引:184
作者
Sun, Yougang [1 ,2 ]
Xu, Junqi [1 ,2 ]
Qiang, Haiyan [1 ,2 ]
Lin, Guobin [1 ,2 ]
机构
[1] Tongji Univ, Coll Transportat Engn, Shanghai 201804, Peoples R China
[2] Tongji Univ, Natl Maglev Transportat Engn R&D Ctr, Shanghai 201804, Peoples R China
基金
国家重点研发计划;
关键词
Dynamic model; maglev train; nonlinear control law; sliding mode control;
D O I
10.1109/TIE.2019.2891409
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The magnetic suspension system of a low-speed maglev train is presented in this paper. The design and realization of the magnetic suspension controller are discussed, and a nonlinear mathematical model of the magnetic suspension system is built. Then, the proportion integration differentiation controller is investigated, which indicates that it is sensitive to disturbances. To reject the disturbance and parameter perturbations, an adaptive neural-fuzzy sliding mode controller is presented, which employs a sliding mode control, adaptive-fuzzy approximator, and the neural-fuzzy switching law. The sufficient simulation and experimental results are included to demonstrate that the presented robust controller significantly reduces the impact of the disturbance and parameter perturbations with a smooth control current.
引用
收藏
页码:8589 / 8599
页数:11
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