[1]顾 洲,鹿世化,朱建忠,等.基于小波基神经网络PID的直流电机伺服控制[J].南京师范大学学报(工程技术版),2006,06(04):017-20.
　GU Zhou~,LU Shihua~,ZHU Jianzhong~,et al.DC Motor’s Servo Controller Based on WNN PID[J].Journal of Nanjing Normal University(Engineering and Technology),2006,06(04):017-20.
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基于小波基神经网络PID的直流电机伺服控制
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
06卷
期数:
2006年04期
页码:
017-20
栏目:
出版日期:
2006-12-30

文章信息/Info

Title:
DC Motor’s Servo Controller Based on WNN PID
作者:
顾 洲1;  鹿世化1;  朱建忠2;  王延维3
1. 南京师范大学动力工程学院, 江苏南京210042;
2. 南京航空航天大学自动化学院, 江苏南京210016;
3. 江苏省标准化研究院, 江苏南京210029
Author(s):
GU Zhou~1; LU Shihua~1; ZHU Jianzhong~2; WNAG Yanwei~3
1.School of Power Engineering,Nanjing Normal University,Nanjng 210042,China;2.School of Automatic Control,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;3.Jiangsu Institute of Standardization,Nanjing 210029,China
关键词:
直流力矩电机;  小波神经网络;  辨识;  自适应PID控制
Keywords:
DC m oto r;  wave le t neura l ne tw ork(WNN);  identification;  adaptiv e PID contro l
分类号:
TM33;TM921.541
摘要:
通过一种新颖的小波基神经网络对未知数学模型的对象进行在线辨识,得到对象的数学模型———Jacob ian信息,并提出了神经网络与模糊控制算法共同在线调整PID参数的方法,从而实现电机位置的准确、快速、实时地跟踪.通过仿真和实验表明:使用该自适应控制方法,能够对位置准确跟踪,基本克服了一般神经网络控制对初始权值的依赖,大大提高了对未知模型的辨识精度,改善了系统的动态响应品质,增强了系统的鲁棒性.
Abstract:
A nove l topo logy ne tw ork o fWNN is introduced to identify on line the object o f a certain unknow n m athe- m atical mode,l and obta in the ob jec t s m athem atica lm odel— the Jacob ian inform ation. A m ethod is suggested o f PID param eters co-ad justed on line byWNN and fuzzy ar ithm e tic, and thus rea lize the accura te, quick and rea ltim e track. The sim together self-tun ing using is reported to track the mo to r s position precise ly. The sim ulation and the experim ent results show that the adaptiv e PID control m ethod can be used to track accura tely the position, and ove rcom e the dependence of genera l neural netw ork on initial we ight, and g reatly increase the prec ision o f identify ing the unknown m ode ,l and im prove the control .

参考文献/References:

[ 1] 高钟毓. 机电控制工程[M ]. 北京: 清华大学出版社, 2002.
GAO Zhongyu. M echan ica l Con tro l Eng ineer ing[M ]. B eijing: T singhua University Press, 2002. ( in Ch inese)
[ 2] 曹先庆, 朱建光, 唐任远. 基于模糊神经网络的永磁同步电动机矢量控制系统[ J]. 中国电机工程学报, 2006( 1): 137-141.
CAO Xianq ing, ZHU Jianguang, TANG Renyuan. Vec to r con tro lled pe rmanent m agne t synch ronous m otor dr ive w ith adaptive fuzzy neural network contro ller[ J]. Proceedings o f the CSEE, 2006( 1): 137-141. ( in Chinese)
[ 3] Q Shourong,WANG Dong feng, PU H an, et a.l G rey predition based RBF neural se lf-tuning PID contro l for turn ing process [ C] / / Proceedmg s o f the Th ird Intem atiod Conference onM ach ine Learn ing and Cyberne tics. Shangha ,i 2004.
[ 4] ZHANG Q, BENVEN IATE A. W av elet netwo rks[ J]. N eural Netw orks, 1992, 3( 6): 889-898.
[ 5] OUSSAR Y, R IVALS I, PERSONNAZL, e t a.l Tra in ing wave le t netwo rks for non linear dynam ic input-output m ode ling [ J]. Neuro compu ting, 1998, 20( 8): 173-188.
[ 6] 胡寿松, 王执铨, 胡维礼. 最优控制理论与系统[M ]. 2版. 北京: 科学出版社, 2005.
HU Shousong, WANG zhiquan, HU w e il.i The Theory and System of Optimum Contro l[M ]. 2 nd ed. Be ijing: Science Press, 2005. ( in Chinese)
[ 7] 易继锴, 侯媛彬. 智能控制技术[M ]. 北京: 北京工业大学出版社, 2004.
Y I Jika,i HOU Yuanb in. Intelligence Contro l [M ]. Be ijing: Be ijing Un iv ers ity o f Techno logy Press, 2004. ( in Ch inese)

备注/Memo

备注/Memo:
作者简介: 顾  洲( 1973-) , 讲师, 主要从事智能控制的教学与研究. E-m ail: guzhoum ail@sohu. com
更新日期/Last Update: 2013-04-29