Abstract
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.
Highlights
Qingyuan maglev train has the advantages of small turning radius, low energy consumption and comfort, which has great potential in the development of urban transportation
The auxiliary power supply system of medium and low speed maglev train is one of the important components of the maglev train electrical system mainly composed of auxiliary converter, charger, suspension power supply, battery pack and load [1,2,3]
Maglev train charger technology mainly realizes the control of battery charging and DC load power supply by combining power electronics, signal processing and automatic control principles [4, 5]
Summary
Qingyuan maglev train has the advantages of small turning radius, low energy consumption and comfort, which has great potential in the development of urban transportation. In order to further improve the output performance of the charger, based on the fuzzy PID control, the genetic algorithm is used to encode and optimize the membership function and fuzzy control rules in the fuzzy control, the optimal membership function distribution and the optimal fuzzy control rules of the charger are determined It has certain research significance for the follow-up research of charger optimization of the Qingyuan maglev train in China. This paper compares the charger output characteristics of different control methods in three cases, and determines the optimization effect of genetic algorithm on Fuzzy PID membership function and fuzzy control rules. The paper compares the output results of the charger under different control methods, and analyzes the advantages of the genetic algorithm to optimize the fuzzy PID control method
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
