引用本文:赵希梅,王浩林,朱文彬.基于自适应模糊控制器和非线性扰动观测器的永磁直线同步电机反馈线性化控制[J].控制理论与应用,2021,38(5):595~602.[点击复制]
ZHAO Xi-mei,WANG Hao-lin,ZHU Wen-bin.Feedback linearization control of permanent magnet linear synchronous motor based on adaptive fuzzy controller and nonlinear disturbance observer[J].Control Theory and Technology,2021,38(5):595~602.[点击复制]
基于自适应模糊控制器和非线性扰动观测器的永磁直线同步电机反馈线性化控制
Feedback linearization control of permanent magnet linear synchronous motor based on adaptive fuzzy controller and nonlinear disturbance observer
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DOI编号  10.7641/CTA.2020.00381
  2021,38(5):595-602
中文关键词  永磁直线同步电动机  反馈线性化控制器  非线性扰动观测器  自适应模糊控制器
英文关键词  permanent magnet linear synchronous motor  feedback linearization controller  nonlinear disturbance observer  adaptive fuzzy controller
基金项目  辽宁省自然科学基金计划重点项目(20170540677)资助.
作者单位E-mail
赵希梅* 沈阳工业大学电气工程学院 zhaoxm_sut@163.com 
王浩林 沈阳工业大学电气工程学院  
朱文彬 沈阳工业大学电气工程学院  
中文摘要
      由于永磁直线同步电机(PMLSM)伺服系统应用于一些高精密场合, 因此克服系统存在的负载扰动、参数 变化等不确定性影响是提高系统性能的关键. 针对不确定性问题, 采用一种基于自适应模糊控制器(AFC)和非线性 扰动观测器(NDO)的反馈线性化控制方法. 首先设计反馈线性化控制器(FLC)实现系统的线性化, 便于位置跟踪; 其 次采用NDO估计并补偿系统的不确定性, 提高跟踪精度. 但在实际运行过程中观测器增益较难选取, 极易产生较大 的观测误差, 为此, 采用AFC方法逼近NDO的观测误差, 通过自适应律动态调整模糊规则, 改善模糊控制器的学习 能力, 增强系统的鲁棒性, 并用李雅普诺夫定理保证系统闭环稳定性. 实验结果表明, 与基于DOB和NDO的反馈线 性化位置控制相比, 该方法能够明显提高系统的跟踪性和鲁棒性.
英文摘要
      A feedback linearization control method based on adaptive fuzzy controller (AFC) and nonlinear disturbance observer (NDO) is proposed for the permanent magnet linear synchronous motor (PMLSM), which is susceptible to nonlinear uncertainties such as external load disturbance, parameter variation and friction. Firstly, a feedback linearization controller (FLC) is designed to linearize the nonlinear system and realize position tracking, so as to stabilize the PMLSM control system. NDO is used to estimate and compensate the uncertainties of the system and reduce the position tracking error of system. However, it is difficult to select the observer gain in the actual operation process, which is very easy to produce large observation error. Therefore, AFC method is used to approach the observation error of NDO, and the fuzzy rules are dynamically adjusted by the adaptive law, so as to improve the learning ability of the fuzzy controller, enhance the robustness of the system, and guarantee the closed-loop stability of the system with Lyapunov theorem. Experiments show that the method not only makes the system have strong robust performance and good tracking accuracy, but also can effectively compensate the uncertainty of the system.