Application Research of Fuzzy PID Control Optimized by Genetic Algorithm in Medium and Low Speed Maglev Train Charger
China’s railways are developing rapidly, and the charger of the auxiliary power supply system is an indispensable part of the maglev train. The existing maglev train charger usually uses the traditional PID controller to control the output of the charger which has a simple structure. To m...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0cd33d7ff28941a8967546e030a3d21f |
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| Sumario: | China’s railways are developing rapidly, and the charger of the auxiliary power supply system is an indispensable part of the maglev train. The existing maglev train charger usually uses the traditional PID controller to control the output of the charger which has a simple structure. To meet the requirements of the maglev train charger output performance which is getting higher and higher, it is necessary to optimize the control strategies which can achieve adaptive control of the system. In this paper, taking Qingyuan maglev train as the research object, the control principle of the charger of the maglev train is studied, and the simulation model of the charger of Qingyuan speed maglev train is built in MATLAB/Simulink. After verifying its feasibility, the paper proposes fuzzy PID control to improve the charger control method, and uses genetic algorithm to optimize fuzzy control membership function and fuzzy rules. Finally, the paper builds a fuzzy PID controller simulation model, compares the output performance of the charger under different control methods, and verifies the superiority of the new control method. |
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