Active Disturbance Rejection Speed Control for Maglev Trains Based on Multiple Population Genetic Algorithm

LI Zikang; DAI Chunhui; HUANG Cuicui; LONG Zhiqiang

doi:10.3969/j.issn.0258-2724.20240113

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Journal of Southwest Jiaotong University > 2025 >

LI Zikang, DAI Chunhui, HUANG Cuicui, LONG Zhiqiang. Active Disturbance Rejection Speed Control for Maglev Trains Based on Multiple Population Genetic Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240113

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LI Zikang, DAI Chunhui, HUANG Cuicui, LONG Zhiqiang. Active Disturbance Rejection Speed Control for Maglev Trains Based on Multiple Population Genetic Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240113

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Active Disturbance Rejection Speed Control for Maglev Trains Based on Multiple Population Genetic Algorithm

doi: 10.3969/j.issn.0258-2724.20240113

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410000, China

Received Date: 07 Mar 2024
Rev Recd Date: 25 Aug 2024

Available Online: 05 Mar 2025

Abstract

Abstract

Compared with traditional wheel-rail transportation, maglev trains have some irreplaceable advantages such as high speed, low noise, smooth operation, and low maintenance cost. To realize precise speed control of maglev trains in complex disturbance environments, an active disturbance rejection control (ADRC) method with self-tuning parameters was proposed. Firstly, the longitudinal dynamic model of maglev trains was established by force analysis to describe the nonlinear hysteresis characteristics of maglev trains during operation. Secondly, the unknown parameters of the model and external disturbances were regarded as the extended state, and a third-order extended state observer was designed to observe the extended state in real time. In addition, the convergence condition of the observer was analyzed based on the Lyapunov stability theorem. Then, to solve the problem of many control parameters and difficult parameter adjustment in traditional ADRC, the multiple population genetic algorithm (MPGA) was introduced to realize adaptive optimization and adjustment of parameters. Finally, the simulation experiment was carried out based on the data collected from the real operation environment of maglev trains, and the simulation results show that compared with traditional ADRC, the speed control accuracy is increased by 22.7% and the tracking stability is improved by 25.6% by means of MPGA-ADRC method, which indicates that the proposed method can effectively improve the stability and ride comfort of maglev trains.
- maglev train,
- active disturbance rejection control,
- extended state observer,
- MPGA,
- speed control

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