A novel fractional-order boundary layer fast terminal sliding mode controller for permanent magnet linear synchronous motor

IF 2.3 3区 工程技术 Q2 ACOUSTICS Journal of Vibration and Control Pub Date : 2024-07-23 DOI:10.1177/10775463241248299
Xinyu Zhao, Limei Wang
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Abstract

This paper presents a novel fractional-order boundary layer fast terminal sliding mode (FBLFTSM) control method for high-precision tracking tasks of the permanent magnet linear synchronous motor (PMLSM). Specifically, a dynamic model of PMLSM with lumped uncertainty is established by considering the tracking task involving parameter variations, disturbance load, etc. Then, based on the dynamic model, a FBLFTSM control law is designed to guarantee higher tracking accuracy of the surface motion than the classical terminal sliding mode control even if the system suffers from unknown disturbance. Meanwhile, the fractional-order boundary layer control has the feature of “large error turns into large gain, small error turns into small gain,” which solves the contradiction between weak chattering and fast convergence in the integer-order boundary layer control and improves the dynamic performance of the system. Finally, the effectiveness of the control approach is verified by conducting tracking experiments on the cSPACE-based motor platform.
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永磁直线同步电机的新型分数阶边界层快速终端滑动模式控制器
本文针对永磁直线同步电机(PMLSM)的高精度跟踪任务,提出了一种新颖的分数阶边界层快速终端滑模(FBLFTSM)控制方法。具体来说,考虑到跟踪任务涉及参数变化、扰动负载等,建立了具有叠加不确定性的 PMLSM 动态模型。然后,基于该动态模型,设计了一种 FBLFTSM 控制法则,即使系统受到未知干扰,也能保证表面运动的跟踪精度高于经典的终端滑模控制。同时,分数阶边界层控制具有 "大误差变大增益,小误差变小增益 "的特点,解决了整数阶边界层控制中弱颤振与快速收敛之间的矛盾,提高了系统的动态性能。最后,通过在基于 cSPACE 的电机平台上进行跟踪实验,验证了控制方法的有效性。
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来源期刊
Journal of Vibration and Control
Journal of Vibration and Control 工程技术-工程:机械
CiteScore
5.20
自引率
17.90%
发文量
336
审稿时长
6 months
期刊介绍: The Journal of Vibration and Control is a peer-reviewed journal of analytical, computational and experimental studies of vibration phenomena and their control. The scope encompasses all linear and nonlinear vibration phenomena and covers topics such as: vibration and control of structures and machinery, signal analysis, aeroelasticity, neural networks, structural control and acoustics, noise and noise control, waves in solids and fluids and shock waves.
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