Artificial neural network identification model of SRM 12-8

摘要：

The proper identification model of the electrical motor very often turns out to be a key factor for the efficient solution of the control task. Artificial neural network description of some of the motor parameters significantly simplifies this identification. This paper particularly deals with identification of the most commonly used switched reluctance motor which has 12 stator poles and 8 rotor poles (SRM 12-8). The key point in this task is the artificial neural network description of the phase inductance and its derivatives in regards to the rotation angle and phase current. The advantages of this description are as follows: The description of the system changes from partial derivative system of equations into ordinary differential equation system. This fact extremely facilitates the Matlab Simulink model simulation. The neural networks easily describe the strong nonlinearities of the identification model.

关键词： switched reluctance motor artificial neural network mathematical modeling

作者: Pavlitov Constantin Chen Hao Gorbounov Yassen Georgiev Tzanko Xing Wang Zan Xiao-shu

作者单位: Technical University of Sofia, Sofia 1000, Bulgaria China University of Mining and Technology, Xuzhou 221116, China

会议类型: 国际会议

会议名称: The 6th International Conference on Mining Science & Technology ICMST 2009(第六届国际矿业科学技术大会)

会议地点: 徐州

会议语种:英文

页码: 1-11

在线出版日期: 2009-10-18（万方平台首次上网日期，不代表论文的发表时间）