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<p>To address the speed regulation challenges stemming from external running resistance variations and internal suspension-induced parameter fluctuations in electromagnetic suspension (EMS) maglev train linear synchronous motor (LSM) drives, this article presents a novel prediction-error-based active disturbance rejection speed control (PADRC). The mathematical model of train motion is established, incorporating lumped process disturbances. Based on this model, a predictive speed error model driven extended state observer (PESO) method is proposed to reject external resistance disturbance. To extract errors dynamics while mitigating noise effects, a recursive least squares (RLS) filter is integrated to proactively predict the variation trends of these disturbances. Besides, an online excitation flux linkage identification scheme is designed to minimize internal suspension-induced disturbances, characterizing the magnetic field variations influenced by excitation current and suspension air-gap. Moreover, stability analysis of the closed-loop system with the proposed scheme is conducted using Lyapunov stability theorem. To finalize, a scaled EMS maglev train LSM test platform is constructed to validate the proposed method. The results demonstrate that the proposed scheme achieves superior speed tracking precision in a real-time control environment, even in low-speed range.</p>

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Prediction-error-based active disturbance rejection speed control for maglev train linear synchronous motor

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Wenbai Zhang,
  • Guobin Lin,
  • Zhiming Liao,
  • Yuanzhe Zhao

摘要

To address the speed regulation challenges stemming from external running resistance variations and internal suspension-induced parameter fluctuations in electromagnetic suspension (EMS) maglev train linear synchronous motor (LSM) drives, this article presents a novel prediction-error-based active disturbance rejection speed control (PADRC). The mathematical model of train motion is established, incorporating lumped process disturbances. Based on this model, a predictive speed error model driven extended state observer (PESO) method is proposed to reject external resistance disturbance. To extract errors dynamics while mitigating noise effects, a recursive least squares (RLS) filter is integrated to proactively predict the variation trends of these disturbances. Besides, an online excitation flux linkage identification scheme is designed to minimize internal suspension-induced disturbances, characterizing the magnetic field variations influenced by excitation current and suspension air-gap. Moreover, stability analysis of the closed-loop system with the proposed scheme is conducted using Lyapunov stability theorem. To finalize, a scaled EMS maglev train LSM test platform is constructed to validate the proposed method. The results demonstrate that the proposed scheme achieves superior speed tracking precision in a real-time control environment, even in low-speed range.