基于粒子群优化算法的磁浮列车自抗扰悬浮控制器研究

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Abstract In order to improve the anti-interference and stability of maglev train suspension system, an active disturbance rejection suspension controller based on particle swarm optimization (PSO) is proposed. Firstly, the suspension system model of single electromagnet is established, and the active disturbance rejection controller is designed based on the model. Finally, the particle swarm optimization is introduced to self-adapt the control parameters, and the active disturbance rejection controller suitable for the system model is obtained. The simulation and single suspension platform test results show that compared with the traditional proportional integral differential (PID) controller, the PSO adaptive auto-disturbance rejection controller has better anti-interference and robustness when the system is disturbed by vertical acceleration and suspension air gap, which provides a new idea for the engineering application of maglev train suspension control algorithm.

Key words： particle swarm optimization (PSO) active disturbance rejection control maglev train suspension control

Received: 04 March 2024

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	Articles by authors
	JIANG Yi
	LIAO Kanqiu
	ZHU Yueou
	TANG Biao

Cite this article:

JIANG Yi,LIAO Kanqiu,ZHU Yueou等. Research on active disturbance rejection suspension controller of maglev train based on particle swarm optimization[J]. Electrical Engineering, 2024, 25(7): 39-44.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I7/39

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