引用本文:于朝阳,赵希梅,金鸿雁.基于非线性扰动观测器的PMLSM自适应反推分数阶积分滑模控制[J].控制理论与应用,2024,41(11):2157~2164.[点击复制]
YU Zhao-yang,ZHAO Xi-mei,JIN Hong-yan.Adaptive backstepping fractional order integral sliding mode control based on nonlinear disturbance observer for PMLSM[J].Control Theory and Technology,2024,41(11):2157~2164.[点击复制]
基于非线性扰动观测器的PMLSM自适应反推分数阶积分滑模控制
Adaptive backstepping fractional order integral sliding mode control based on nonlinear disturbance observer for PMLSM
摘要点击 197  全文点击 39  投稿时间：2022-10-21  修订日期：2024-07-17
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DOI编号  10.7641/CTA.2023.20918
  2024,41(11):2157-2164
中文关键词  永磁直线同步电机  不确定性因素  自适应反推分数阶积分滑模控制  非线性扰动观测器  鲁棒性
英文关键词  permanent magnet linear synchronous motor  uncertain factors  adaptive backstepping fractional integral sliding mode control: nonlinear disturbance observer  robustness
基金项目  辽宁省博士科研启动基金计划项目(2022–BS–177)资助.
作者单位E-mail
于朝阳 沈阳工业大学电气工程学院 hxzlss19870310@163.com 
赵希梅* 沈阳工业大学电气工程学院 zhaoxm_sut@163.com 
金鸿雁 沈阳工业大学电气工程学院  
中文摘要
      为提高永磁直线同步电机(PMLSM)伺服系统的位置跟踪精度, 克服其易受外部扰动、参数变化、摩擦力等不确定因素影响的问题, 本文提出了一种基于非线性扰动观测器(NDO)的自适应反推分数阶积分滑模(ABFOSMC)控制方法. 首先, 对PMLSM建立动态数学模型. 其次, 针对PMLSM的非线性特性, 采用反推控制设计虚拟控制函数, 实现系统位置的精准跟踪, 并结合分数阶积分滑模控制方法增强系统的鲁棒性. 最后, 引入NDO估计系统不确定性, 将观测结果动态补偿到ABFOSMC中, 有效降低不确定性因素对系统伺服性能的影响, 提高位置跟踪精度.实验结果显示, 所提出的方法切实可行, 有效提高了系统鲁棒性能和跟踪性能.
英文摘要
      In order to improve the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system and overcome the problems that it is easy to be affected by uncertain factors such as parameter change, load disturbance and friction, an adaptive backstepping fractional integral sliding mode control (ABFOSMC) method based on nonlinear disturbance observer (NDO) is proposed. Firstly, the dynamic mathematical model of PMLSM is established. Secondly, according to the nonlinear characteristics of PMLSM, the backstepping control is used to design the virtual control function to realize the accurate position tracking of the system, and the fractional integral sliding mode control is combined to enhance the robustness of the system. Finally, NDO is introduced to estimate system uncertainty and dynamically compensate the observation results to ABFOSMC, effectively reducing the impact of uncertainty factors on system servo performance and improving position tracking accuracy. Experimental results show that the proposed method is feasible and effectively improves the tracking performance.