电工技术学报  2020, Vol. 35 Issue (12): 2545-2553    DOI: 10.19595/j.cnki.1000-6753.tces.190308
电机与电器 |
基于径向基函数神经网络的永磁直线同步电机反推终端滑模控制
付东学, 赵希梅
沈阳工业大学电气工程学院 沈阳 110870
Backstepping Terminal Sliding Mode Control Based on Radial Basis Function Neural Network for Permanent Magnet Linear Synchronous Motor
Fu Dongxue, Zhao Ximei
School of Electrical Engineering Shenyang University of Technology Shenyang 110870 China
全文: PDF (46275 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 为解决永磁直线同步电机(PMLSM)伺服系统位置跟踪精度易受参数变化、负载扰动、摩擦力等不确定性因素影响的问题,该文提出一种基于径向基函数(RBF)神经网络反推终端滑模控制方法。首先,建立含有不确定性的PMLSM动态数学模型。然后,采用反推终端滑模控制将系统状态在有限时间内收敛到平衡点,提高系统的响应速度;为了进一步削弱抖振现象,利用双曲正切函数与边界层厚度相结合来设计饱和函数,以取代符号函数;并且利用RBF神经网络去逼近系统中存在的不确定性,进而获得快速的跟踪性能和较强的抗扰能力。最后,实验结果表明,所提出的控制方法不仅改善了系统的跟踪性和鲁棒性,而且明显削弱了抖振问题。
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
付东学
赵希梅
关键词 永磁直线同步电机反推终端滑模控制径向基函数神经网络抖振鲁棒性    
Abstract:The position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is susceptible to parameter variation, load disturbance, friction and other uncertain factors. Therefore, a backstepping terminal sliding mode control method based on radial basis function (RBF) neural network is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the state of the system converges to the equilibrium point in a finite time and the response speed of the system is improved by using the backstepping terminal sliding mode control. In order to further weaken chattering phenomenon, the saturation function is designed combined the hyperbolic tangent function with boundary layer thickness to replace the signum function. Moreover, RBF neural network is used to approximate the uncertainties in the system, and then fast-tracking performance and strong immunity ability are obtained. Finally, the experimental results show that the proposed control method not only improves the tracking and robustness of the system, but also significantly weakens the chattering problem.
Key wordsPermanent magnet linear synchronous motor    backstepping terminal sliding mode control    radial basis function (RBF) neural network    chattering    robustness   
收稿日期: 2019-03-25     
PACS: TP273  
  TM351  
基金资助:辽宁省自然科学基金计划重点项目(20170540677)和辽宁省教育厅科学技术研究项目(LQGD2017025)资助
通讯作者: 赵希梅 女,1979年生,教授,博士生导师,研究方向为电机控制、机器人控制、智能控制等。E-mail: zhaoxm_sut@163.com   
作者简介: 付东学 男,1992年生,博士研究生,研究方向为电机控制、智能控制。E-mail: 18629974521@163.com
引用本文:   
付东学, 赵希梅. 基于径向基函数神经网络的永磁直线同步电机反推终端滑模控制[J]. 电工技术学报, 2020, 35(12): 2545-2553. Fu Dongxue, Zhao Ximei. Backstepping Terminal Sliding Mode Control Based on Radial Basis Function Neural Network for Permanent Magnet Linear Synchronous Motor. Transactions of China Electrotechnical Society, 2020, 35(12): 2545-2553.
链接本文:  
https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.190308          https://dgjsxb.ces-transaction.com/CN/Y2020/V35/I12/2545