|本期目录/Table of Contents|

[1]彭熙伟,高瀚林.永磁同步电机的改进对角递归神经网络PI 控制策略[J].电机与控制学报,2019,23(04):126-132.[doi:10.15938/j.emc.2019.04.016]
 PENG Xi-wei,GAO Han-lin. Improved diagonal recursion neural network and PI control of permanent magnet synchronous motor[J].,2019,23(04):126-132.[doi:10.15938/j.emc.2019.04.016]
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永磁同步电机的改进对角递归神经网络PI 控制策略
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
23
期数:
2019年04
页码:
126-132
栏目:
出版日期:
2019-04-15

文章信息/Info

Title:
 Improved diagonal recursion neural network and PI control of permanent magnet synchronous motor
作者:
 彭熙伟1 高瀚林2
 ( 1. 北京理工大学自动化学院,北京100081; 2. 中国舰船研究设计中心,武汉430060)
Author(s):
 PENG Xi-wei1 GAO Han-lin2
 ( 1. School of Automation,Beijing Institute of Technology,Beijing 100081,China;2. China Ship Development and Design Center,Wuhan 430060,China)
关键词:
 伺服系统 永磁同步电机 神经网络 比例积分控制 计算机仿真
Keywords:
 servo systems permanent magnet synchronous motor neural nets PI computer simulation
分类号:
TP 273
DOI:
10.15938/j.emc.2019.04.016
文献标志码:
A
摘要:
  针对采用传统PI 控制器的永磁同步电机交流伺服系统无法兼顾良好的速度响应性能和抗干扰能力的问题,提出一种将对角递归神经网络( DRNN) 与PI 控制相结合的控制算法,并引入学习率动态调整的思想对算法进行改进,解决固定学习率DRNN 算法无法兼顾系统稳定性和较快学习速率的问题。建立永磁同步电机的仿真实验模型,并对传统PI 控制器、固定学习率以及学习率可动态调整的DRNN-PI 控制器的实验效果进行综合对比与分析,验证了采用改进后控制器的永磁同步电机交流伺服系统能够实现速度曲线无超调且不受负载转矩突变影响的良好控制效果。
Abstract:
For the problem that the permanent magnet synchronous motor AC servo system with traditional PI controller cannot strike a balance between good response performance and strong robustness,a control algorithm combining diagonal recurrent neural network ( DRNN) and PI control was proposed. In addition,the idea of dynamic adjustment of the learning rate was introduced to improve the algorithm,which solves the problem that the DRNN algorithm cannot strike a balance between system stability and fast learning rate. The simulation model was constructed,and the comparison experiment between PI controller,DRNN-PI controller with fixed learning rate and DRNN-PI controller with variable learning rate was carried out. The experiment shows that the AC servo system using DRNN-PI controller with variable learning rate has a good speed performance. There is no overshoot in the speed curve,and the speed is not affected by load fluctuation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2017 - 05 - 24
基金项目: 国家自然科学基金( 61304026)
作者简介: 彭熙伟( 1966—) ,男,博士,教授,研究方向为检测与智能控制;
高瀚林( 1989—) ,男,硕士,研究方向为检测与智能控制。
通信作者: 彭熙伟
更新日期/Last Update: 2019-06-29