摘要
针对执行重复任务的永磁直线同步电机(PMLSM)在迭代学习过程中易受负载扰动、参数变化等非重复性扰动的影响而难以实现高性能跟踪控制的问题,提出了一种迭代学习控制(ILC)与变论域模糊控制相结合的分段变论域模糊ILC方法。在误差较大的时间段,采用变论域模糊控制实时地改变ILC的学习增益,并智能地调整模糊控制的论域,抑制不确定性因素对系统的影响,提高控制精度;在误差较小的时间段,采用PD型ILC,使学习增益稳定,进一步减小位置误差。实验结果表明,该控制方法可以有效地加快收敛速度,提高位置跟踪精度,并增强系统的鲁棒性。
For permanent magnet linear synchronous motor （PMLSM） to perform repetitive tasks, in the process of iterative learning, it is vulnerable to the influence of the non repetitive disturbances such as the load disturbances, parameter variation and so on. It is difficult to achieve high performance tracking control. The segmented variable universe fuzzy iterative learning control （ILC） is proposed which combines ILC with variable universe fuzzy control. In the period of larger error, the variable universe fuzzy control is used to change the learning gain of ILC in real time, and intelligently adjust the domain of fuzzy control, in order to restrain the influence of uncertain factors on the system and improve the control precision. In the period of smaller error, PD type ILC is used to make the learning gain stable and the position error can be reduced further. The experimental results show that the control method can effectively speed up the convergence rate, improve the position tracking accuracy, and enhance the robustness of the system.
出处
《电工技术学报》
EI
CSCD
北大核心
2017年第23期9-15,共7页
Transactions of China Electrotechnical Society
基金
国家自然科学基金项目(51175349)
辽宁省自然科学基金项目(20170540677)资助
关键词
永磁直线同步电机
迭代学习控制
分段变论域模糊控制
跟踪精度
Permanent magnet linear synchronous motor, iterative learning control, segmented variable universe fuzzy control, tracking accuracy
