|本期目录/Table of Contents|

[1]赵希梅,王晨光.永磁直线同步电机的智能增量滑模控制[J].电机与控制学报,2017,21(09):47-54.[doi:10.15938/j.emc.2017.09.007]
 ZHAO Xi-mei,WANG Chen-guang.Intelligent incremental sliding mode control for permanent magnet linear synchronous motor[J].,2017,21(09):47-54.[doi:10.15938/j.emc.2017.09.007]
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
21
期数:
2017年09
页码:
47-54
栏目:
出版日期:
2017-09-01

文章信息/Info

Title:
Intelligent incremental sliding mode control for permanent magnet linear synchronous motor
作者:
?赵希梅 王晨光
?(沈阳工业大学 电气工程学院,辽宁 沈阳 110870)
Author(s):
?ZHAO Xi-mei WANG Chen-guang
?(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China)
关键词:
永磁直线同步电机 智能增量滑模控制 小波神经网络 改进粒子群优化算法 位置跟踪
Keywords:
permanent magnet linear synchronous motors intelligent incremental sliding mode control wavelet neural network improved particle swarm optimization position tracking
分类号:
TM 315
DOI:
10.15938/j.emc.2017.09.007
文献标志码:
A
摘要:
?针对永磁直线同步电机直接驱动伺服系统的位置跟踪精度易受参数变化、外部扰动、端部效应等不确定性因素的影响,提出了一种将小波神经网络( wavelet neural network,WNN) 和增量滑模控制器相结合的智能增量滑模控制方法。利用系统先前的状态信息和控制动作作为反馈量,同时选择饱和函数作为切换函数来设计增量滑模控制器,不仅削弱了抖振,而且提高了系统的跟踪性能; 利用 WNN 实时观测和补偿参数变化和外部扰动等影响,并采用改进的粒子群优化算法在线调整 WNN 的学习率,对不确定因素进行实时估计。从理论上分析证明了此控制器可以保证系统收敛,提高了直线伺服系统的控制性能。通过系统实验,证明了所提出方案的有效性,与滑模控制( sliding mode control,SMC) 相比,系统具有强鲁棒性和良好的位置跟踪精度,明显地削弱了抖振现象。
Abstract:
Intelligent incremental sliding mode control (IISMC) method combining wavelet neural network (WNN) with incremental sliding mode controller was proposed for permanent magnet linear synchronous motor (PMLSM) direct-drive servo system of which the precision of position tracking is vulnerable to the influence of the uncertainties,such as parameter variations,external disturbances,inherent end effect and so on. First,incremental sliding mode controller was designed by utilizing previous state information and control action,and adopting the saturated function as the switching function. The proposed controller achieves high precision tracking and chattering reduction. Then,to observe and compensate the influence such as parameter variation and external disturbances,while estimating uncertain parameter in real time, WNN was investigated,which used an improved particle swarm optimization (IPSO) algorithm to adjust the learning rate online. In theory,the proposed controller is proved to guarantee the convergence of the system tracking error. Finally,system experimental results confirm effectiveness of the proposed method. Compared with the sliding mode control ( SMC),the system has accurate position tracking performance and strong robustness,meanwhile,the chattering is obviously reduced.

参考文献/References:

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备注/Memo

备注/Memo:
?收稿日期: 2016 - 04 - 08
?基金项目: 国家自然科学基金( 51175349) ; 辽宁省自然科学基金( 20170540677)
作者简介: 赵希梅( 1979—) ,女,博士,副教授,研究方向为直线伺服、数控、鲁棒控制等; 王晨光( 1990—) ,男,硕士研究生,研究方向为直线伺服、智能控制。
更新日期/Last Update: 2018-02-06