A modeling method of SRM based on RBF neural networks
上传者:田广志|上传时间:2015-05-11|密次下载
A modeling method of SRM based on RBF neural networks
A Modeling Method of SRM Based on RBF Neural
Networks
Shufen Qi
College of Automation and Electronic Engineering Qingdao University of Science and Technology
Qingdao, Shandong Province, China
qsf16@http://wendang.chazidian.com
Abstract—This paper presents a modeling method of Switched Reluctance Motor (SRM) based on the Radial Basis Function (RBF) Neural Networks. By analysing measuring data and nonlinear characteristics of SRM, the modeling of SRM is designed with Gaussion Function. The simulated results show that the proposed model has better capability of generalization and correctly represents the characteristics of SRM compared with traditional method of local linearization or BP Neural Networks, which is more significative to real-time control for SRM.
Keywords-SRM; RBF Neural Networks; Modeling
Hui Kong
College of Automation and Electronic Engineering Qingdao University of Science and Technology
Qingdao, Shandong Province, China
Kong_hui2009@http://wendang.chazidian.comArtificial Neural Network (ANN) techniques have grown rapidly in recent years [3]. Extensive research has been carried out on the application of artificial intelligence. Radial Basis Functions emerged as a variant of artificial neural network in late 80’s. However, their roots are entrenched in much older pattern recognition techniques as for example potential functions, clustering, functional approximation, spline interpolation and mixture models. Due to their nonlinear approximation properties, RBF neural networks are able to model complex mappings [4].
This paper investigates the use of RBF neural networks for the modelling of the magnetic nonlinearity of the SRM. Since this method does not require any prior information I. INTRODUCTION
regarding the SRM system apart from the input and output
An important characteristic of the SRM drive is its inherent
signals, it is quite simple and cost effective. The modelling
nonlinearity. The inductance of the magnetic circuit is a
method in this paper departs significantly from previous
nonlinear function of both phase current and rotor position [1].
modelling method by the authors, in which the magnetisation
In addition, the system handles energy most efficiently when
curves are represented by functions of flux linkage against
the energy conversion cycles are made as square as possible,
rotor position, rather than current. In the paper, first, magnetic
maximising the ratio of energy converted to energy input. This
nonlinearity of the SRM is presented, then RBF neural
leads a particularly difficult problem because of their
network approach to the modelling of the SRM is presented.
complicated magnetic circuit, which operates at varying levels
RBF neural network training requirements are discussed next
of saturation under operating conditions. Square energy
and finally, the models are verified through comparisons with
conversion cycles are created by driving the motor into
experimentally measured results.
magnetic saturation and bring the energy handling requirements of inverter into closer alignment with the energy
II. MAGNETIC NONLINEARITIES OF THE SRM conversion characteristics of motor. This can results in reduced
switch requirements and energy savings. The recirculated A cross section of 8/6 SRM is shown in Fig. 1, in which energy in a drive with an applied voltage requires current flow both the stator and the rotor are salient poles. The stator and acts to increase the inverter and motor losses that winding consists of a set of coils, each of which is wound on accompany the current flow. Some relevant papers proposed a one pole. The reluctance of the flux path between the two quite successful method to model the flux linkage as a function diametrically opposite stator poles varies as a pair of rotor of current and rotor position. This method has been modified poles rotates in and out of alignment. Since inductance is by several others. And some authors have also proposed a inversely proportional to reluctance, the inductance of a phase method to provide analytical expressions for the flux linkage winding is a maximum when the rotor is in the aligned and current for every rotor position within a single summary position and a minimum when the rotor is in the non-aligned equation. In contrast to the above methods, there have been position. The rotor teeth tend to align with an energized phase many attempts to generate the necessary static magnetisation in order to minimize the reluctance path [1]. curves by Finite Element Analysis (FEA) [2]. Recently, the
The production of torque of the SRM depends upon the authors have reported an application of RBF neural networks
stator current magnitude regardless of the direction [5]. The for modelling of the magnetic nonlinearity of the magnetisation
magnetic torque of SRM is a kind of reluctance torque. The curves.
direction of rotation is irrelevant to direction of current and
978-1-4577-0321-8/11/$26.00 ©2011 IEEE
44
内容需要下载文档才能查看
elements. Because of structure of SRM and the nonlinearity of magnetic circuit, both the inductance of winding and the flux linkage are a nonlinear function of current and rator position. SRM models are generally made up of three parts: the electrical model, torque characteristics and mechanical model. The electrical circuit for one phase of SRM is shown in Fig. 2. Applying Kirchhoff voltage law and ignoring hysteresis, eddy current and the mutual inductance between the windings thus voltage given by (1) [6].
内容需要下载文档才能查看v=ir+
dψi,θ (1) dt
However, one of most important purpose of measuring flux linkage of SRM is to analyse characteristics of the torque. We can work out the torque characteristics on the basis of the flux linkage characteristics as shown by (2) (3) (4).
?W' (2) ?θi=const
T=
W'=
³ψ(θ,i)di (3)
i
‘T’ is electromagnetic torque. ‘W'’ is magnetic field coenergy.
The expression formula of the torque can be figured out by substituting (3) in (2) as follows:
T=
?§i
(4) ¨³0ψ(θ,i)di·¸
¹i=const?©
Where ‘v’ is voltage across phase winding
‘i’ is phase current
‘r’ is resistance of the phase winding ‘? ’ is flux linkage. ‘θ’ is rotor angle.
In addition, the torque characteristics is a influencing
内容需要下载文档才能查看A group of actual measurement data given in Tab. I was measured through a experiment and the SRM we use in this experiment is a 4-phase 8/6 motor. In Tab. I, ‘i’ is phase
45
内容需要下载文档才能查看
considered in various scientific fields. The Gaussian activation function for RBF networks is given by:
φj(X)=exp[?(X?μj)T¦-j1(X?μj)] (5)
For j = 1,…, L, where X is the input feature vector, L is the number of hidden units, ? and ?? are the mean and the covariance matrix of the jth Gaussian function.
The output layer implements a weighted sum of hidden-unit outputs:
ψ(X)=¦λjk?j(X) (6)
j=1
L
measured every 5° angle of rotor in a cycle. The actual
内容需要下载文档才能查看measurement data show that the flux linkage is a monotone increasing function when the rotor angle changes from 0° to 30° and is a monotone decreasing function when the rotor angle changes from 30° to 0°. The flux linkage curves are drawn based on the measuring data and shown in Fig. 3. Based on the general equation of SRM and the actual measurement data, the model of SRM is designed by using the gradual approaching method of Artificial Neural Network modeling.
III. NEURAL NETWORK MODELLING OF THE SRM Ability and adaptability to learn, generalisation, less information requirement, fast real-time operation and ease of implementation have made ANNs popular in the last few years. ANNs have been applied in many areas. Dynamic system modelling, identification and control using ANNs are particularly very promising. As a result of that, the modelling of SRM has been employed using the RBF neural network, which is the most popular algorithm in the arena of neural networks [3].
A. RBF Neural Networks
RBF are embedded in a two layer neural network, where each hidden unit implements a radial activated function. The output units implement a weighted sum of hidden unit outputs. The input into an RBF network is nonlinear while the output is linear [7]. Due to their nonlinear approximation properties, RBF networks are able to model complex mappings, which perceptron neural networks can only model by means of multiple intermediary layers [8].
In order to use a Radial Basis Function Network we need to specify the hidde unit activation function, the number of processing units, a criterion for modeling a given task and a training algorithm for finding the parameters of the network. Various functions have been tested as activation functions for RBF networks. Mixtures of Gaussians have been
For k = 1,…M, ?jk where are the output weights, each corresponding to the connection between a hidden unit and an output unit and M represent the number of output units. The weights ?jk show the contribution of a hidden unit to the respective output unit.
In order to model such a mapping we have to find the network weights and topology. Finding the RBF weights is called network training [9]. If we have at hand a set of input-output pairs, called training set, we optimize the network parameters in order to fit the network outputs to the given inputs. The fit is evaluated by means of a cost function, usually assumed to be the mean square error. After training, the RBF network can be used with data whose underlying statistics is similar to that of the training set. On-line training algorithms adapt the network parameters to the changing data statistics. RBF networks have been successfully applied to various of complex nonlinear modeling, thereinto, the modeling of SRM is a good application example.
B. RBF Network used in Modelling
The RBF network used in modelling is shown in Fig. 4 with a block diagram. The training set used a group of actual measurement data given in Tab. I. This structure was used for training and testing processes. After a couple of training, it was found that only one layer network can achieve the mapping task in high accuracy. The both learning and momentum coefficients were 0.004 and the number of epoch was 100 for training. The most suitable network configuration found was 2x6x1.
46
内容需要下载文档才能查看
knowledge is required (model or equation), reduced mathematical complexity, and faster operation after training. What’s more, this modeling method presented in this paper has reduced the learning time of network and the number of epoch compared to BP neural network. The model can be based for the analysis and design of the control system of SRM.
REFERENCES
[1] Honghua Wang, “Technology of drive control for Switched Reluctance Motors”[M]. Beijing; Pass of Mechanical industry, 1995. (in Chinese)
[2] Rakesh Saxena, Bhim Singh and Yogesh Pahariya. “Measurement of
Flux Linkage and Inductance Profile of SRM” [J]. International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April, 2010. pp. 389-393
[3] Elmas C, Sagiroglu S, Colak I, et al . “Nonlinear modeling of a switched
reluctance drive based on neural networks.” Proceedings of Fifth International Conference on Power Electronics and Variable-Speed Drives. London LTK. pp. 7-12, October, 1994.
[4] Reay D, Williams B W, “Sensorless position detection using neural
networks for the control of switched reluctance motors.” Proceeding of the 1999 IEEE International conference on control applications, pp. 1073-1077, 1999
[5] Tian Hua and Ching Gua Chen, “Implementation of a Sensorless
Switched Reluctance Drive with Self Inductance Estimating Technique” IEEE Industrial Electronics Conference, pp. 508-513, 2002. (in Chinese)
[6] Liangwen Ji and Jingping Jiang, “Modeling of Switched Reluctance
Motors Based on Radial Basis Function Neural Networks,” Academic journal of electrotechnics, pp.7-11, April, 2001.
[7] Xia C L, Xue M, Chen W and Xie X M. “Flux Linkage Characteristic
Measurement and Parameter Identification Based on Hybrid Genetic Algorithm for Switched Reluctance Motors.” IEEE Conference on Industrial Electronics and applications (ICIEA), Singapore. pp. 1619-1623, 2008
[8] Nakamura K, Fujio S, Ichinokura O. “A method for calculating iron loss
of an SR motor based on reluctance network analysis and comparison of symmetric and asymmetric excitation.” IEEE Transactions on Magnetics, pp. 3440-3442, 2006
[9] RayW F, Erfan F,” A New Method of Flux or Inductance Measurement
for Switched Reluctance Motors”, Proceedings of the 5th International Conferenceon Power Electronics and Variable -Speed Drives[C]. Oct, 1994, London, UK . pp. 137-140. neural networks is shown in Fig 5. In this paper, the flux linkage model is designed by the actual measurement data of SRM and its result is obtained by off-line training. In the applied process, the value of ? can be got by measuring the input valuse of θang i. Then on the basis of (4), the torque can be obtained and the on-line control can be achieved. Fig. 5 shows the variation of flux linkage with current along with RBF neural network results. These results have also demonstrated the strong potential of the RBF neural network applied to the SRM. Fig. 3 shows an actual measuring result obtained by a data acquisition board. Contrasting Fig. 5 and Fig. 3, there is generally good agreement between simulation and experimental results. IV. CONCLUSION Simulation results were verified through experimental results and the modeling of SRM based on RBF neural network was proven to be reasonably accurate. The advantages of the model developed here are that no a priori
47
下载文档
热门试卷
- 2016年四川省内江市中考化学试卷
- 广西钦州市高新区2017届高三11月月考政治试卷
- 浙江省湖州市2016-2017学年高一上学期期中考试政治试卷
- 浙江省湖州市2016-2017学年高二上学期期中考试政治试卷
- 辽宁省铁岭市协作体2017届高三上学期第三次联考政治试卷
- 广西钦州市钦州港区2016-2017学年高二11月月考政治试卷
- 广西钦州市钦州港区2017届高三11月月考政治试卷
- 广西钦州市钦州港区2016-2017学年高一11月月考政治试卷
- 广西钦州市高新区2016-2017学年高二11月月考政治试卷
- 广西钦州市高新区2016-2017学年高一11月月考政治试卷
- 山东省滨州市三校2017届第一学期阶段测试初三英语试题
- 四川省成都七中2017届高三一诊模拟考试文科综合试卷
- 2017届普通高等学校招生全国统一考试模拟试题(附答案)
- 重庆市永川中学高2017级上期12月月考语文试题
- 江西宜春三中2017届高三第一学期第二次月考文科综合试题
- 内蒙古赤峰二中2017届高三上学期第三次月考英语试题
- 2017年六年级(上)数学期末考试卷
- 2017人教版小学英语三年级上期末笔试题
- 江苏省常州西藏民族中学2016-2017学年九年级思想品德第一学期第二次阶段测试试卷
- 重庆市九龙坡区七校2016-2017学年上期八年级素质测查(二)语文学科试题卷
- 江苏省无锡市钱桥中学2016年12月八年级语文阶段性测试卷
- 江苏省无锡市钱桥中学2016-2017学年七年级英语12月阶段检测试卷
- 山东省邹城市第八中学2016-2017学年八年级12月物理第4章试题(无答案)
- 【人教版】河北省2015-2016学年度九年级上期末语文试题卷(附答案)
- 四川省简阳市阳安中学2016年12月高二月考英语试卷
- 四川省成都龙泉中学高三上学期2016年12月月考试题文科综合能力测试
- 安徽省滁州中学2016—2017学年度第一学期12月月考高三英语试卷
- 山东省武城县第二中学2016.12高一年级上学期第二次月考历史试题(必修一第四、五单元)
- 福建省四地六校联考2016-2017学年上学期第三次月考高三化学试卷
- 甘肃省武威第二十三中学2016—2017学年度八年级第一学期12月月考生物试卷
网友关注
- 詹姆斯库克大学生活攻略好不好
- 人称代词和物主代词Word
- 河北省专接本考生报名表
- 失掉自信力了吗 初中语文教案
- 2017年5月广西公需科目考试答案
- 消防常识题库答案
- 清兵卫与葫芦 初中语文教案
- 格拉纳多斯作品37-5《西班牙舞曲-安达路西亚》Danza Espanola-Andaluza,Op37,No. 5;E.Granados古典吉他谱
- 瓶花教案设计
- 结构安装工程Word
- 差半车麦秸 初中语文教案
- 阳光海岸大学专业如何
- 小课题研究方案
- 汽车专业毕业设计 汽车空调常见故障与维护
- 安全培训Word
- 东大17春学期《公共政策学》在线作业2满分答案
- 矿泉水瓶的悲哀
- 少了快乐
- 詹姆斯库克大学录取率好吗
- 安全培训Word
- 新英格兰大学学分修习好吗
- 论文投稿方式怎样才适合自己?
- 新能源汽车应用技术电子教案大全
- 詹姆斯库克大学COE要多久能拿到
- 尘欢——终于算是认识了呢
- 思恋
- 最新廉政文化建Word
- 安全大如天
- 减低成本Word
- 廉颇蔺相如列传Word
网友关注视频
- 沪教版牛津小学英语(深圳用) 六年级下册 Unit 7
- 19 爱护鸟类_第一课时(二等奖)(桂美版二年级下册)_T502436
- 冀教版英语四年级下册第二课
- 三年级英语单词记忆下册(沪教版)第一二单元复习
- 苏科版数学 八年级下册 第八章第二节 可能性的大小
- 【部编】人教版语文七年级下册《逢入京使》优质课教学视频+PPT课件+教案,安徽省
- 青岛版教材五年级下册第四单元(走进军营——方向与位置)用数对确定位置(一等奖)
- 第19课 我喜欢的鸟_第一课时(二等奖)(人美杨永善版二年级下册)_T644386
- 化学九年级下册全册同步 人教版 第22集 酸和碱的中和反应(一)
- 【获奖】科粤版初三九年级化学下册第七章7.3浓稀的表示
- 外研版英语三起6年级下册(14版)Module3 Unit2
- 外研版英语三起5年级下册(14版)Module3 Unit2
- 【部编】人教版语文七年级下册《老山界》优质课教学视频+PPT课件+教案,安徽省
- 苏科版数学七年级下册7.2《探索平行线的性质》
- 【部编】人教版语文七年级下册《泊秦淮》优质课教学视频+PPT课件+教案,湖北省
- 二年级下册数学第一课
- 二年级下册数学第三课 搭一搭⚖⚖
- 北师大版小学数学四年级下册第15课小数乘小数一
- 外研版英语七年级下册module3 unit1第二课时
- 沪教版八年级下册数学练习册21.4(1)无理方程P18
- 外研版英语七年级下册module3 unit2第一课时
- 沪教版牛津小学英语(深圳用) 四年级下册 Unit 8
- 每天日常投篮练习第一天森哥打卡上脚 Nike PG 2 如何调整运球跳投手感?
- 【部编】人教版语文七年级下册《逢入京使》优质课教学视频+PPT课件+教案,辽宁省
- 【部编】人教版语文七年级下册《过松源晨炊漆公店(其五)》优质课教学视频+PPT课件+教案,辽宁省
- 外研版八年级英语下学期 Module3
- 外研版英语七年级下册module3 unit2第二课时
- 北师大版数学四年级下册3.4包装
- 化学九年级下册全册同步 人教版 第18集 常见的酸和碱(二)
- 化学九年级下册全册同步 人教版 第25集 生活中常见的盐(二)
精品推荐
- 2016-2017学年高一语文人教版必修一+模块学业水平检测试题(含答案)
- 广西钦州市高新区2017届高三11月月考政治试卷
- 浙江省湖州市2016-2017学年高一上学期期中考试政治试卷
- 浙江省湖州市2016-2017学年高二上学期期中考试政治试卷
- 辽宁省铁岭市协作体2017届高三上学期第三次联考政治试卷
- 广西钦州市钦州港区2016-2017学年高二11月月考政治试卷
- 广西钦州市钦州港区2017届高三11月月考政治试卷
- 广西钦州市钦州港区2016-2017学年高一11月月考政治试卷
- 广西钦州市高新区2016-2017学年高二11月月考政治试卷
- 广西钦州市高新区2016-2017学年高一11月月考政治试卷
分类导航
- 互联网
- 电脑基础知识
- 计算机软件及应用
- 计算机硬件及网络
- 计算机应用/办公自动化
- .NET
- 数据结构与算法
- Java
- SEO
- C/C++资料
- linux/Unix相关
- 手机开发
- UML理论/建模
- 并行计算/云计算
- 嵌入式开发
- windows相关
- 软件工程
- 管理信息系统
- 开发文档
- 图形图像
- 网络与通信
- 网络信息安全
- 电子支付
- Labview
- matlab
- 网络资源
- Python
- Delphi/Perl
- 评测
- Flash/Flex
- CSS/Script
- 计算机原理
- PHP资料
- 数据挖掘与模式识别
- Web服务
- 数据库
- Visual Basic
- 电子商务
- 服务器
- 搜索引擎优化
- 存储
- 架构
- 行业软件
- 人工智能
- 计算机辅助设计
- 多媒体
- 软件测试
- 计算机硬件与维护
- 网站策划/UE
- 网页设计/UI
- 网吧管理