Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System

doi:10.11908/j.issn.0253-374x.20058

Home > Archive>Volume 48, Issue 9, 2020 >1344-1352. DOI:10.11908/j.issn.0253-374x.20058

Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System
DOI:
                        10.11908/j.issn.0253-374x.20058
                    
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Author:
                        CHEN Chen1,2,3CHEN Chen
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China;College of Transportation, Tongji University, Shanghai 201804, China
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XU Junqi2XU Junqi
Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
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NI Fei2NI Fei
Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
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LIN Guobin2LIN Guobin
Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
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WU Han4WU Han
Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics of Chinese Academy of Sciences, Beijing 100109, China
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Affiliation:1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China;3.College of Transportation, Tongji University, Shanghai 201804, China;4.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics of Chinese Academy of Sciences, Beijing 100109, China
Clc Number:TP273
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Abstract:

An active control strategy of maglev train suspension system based on artificial intelligence load estimation system is proposed in this paper. Firstly, the mathematical model of single-point levitation is given, and the open-loop instability is proven by the Routh-Herwitz criterion. Secondly, considering the load characteristics and the real-time suspension changes, a multi-layer artificial neural network is constructed to control the output of the control variables for the suspension system. Thirdly, the non-dominated sorting genetic algorithm (NSGA) is used to optimize the system parameters. The results show that the proposed control method has better robustness and can still keep relatively small error under large load disturbance.

Key words:maglev train;nonlinear model of levitation system;artificial neural network;load estimation;active control

Reference

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CHEN Chen, XU Junqi, NI Fei, LIN Guobin, WU Han. Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System[J].同济大学学报(自然科学版),2020,48(9):1344~1352

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Received:February 24,2020
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Online: September 27,2020
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