Torque Ripple Minimization in Switched Reluctance Motor Basedon BP Neural Network

摘要：

The instantaneous torque control for torque ripple minimization of switched reluctance motor (SRM) by BP neural network is presented. As SRM has a highly nonlinear characteristics, neural network is well suited for its control. After static torque characteristics of SRM having been measured, the torque model and the inverse torque model are developed based on BP neural network of Levenberg-Marquardt algorithm. The torque ripple minimization can be achieved by optimum profiling of the phase current based on instantaneous torque control. An efficient commutation strategy for minimizing torque ripple as well as avoiding power converter voltage saturation over a wide speed range of operation is proposed. Simulation results verify the feasibility of this torque ripple minimization technique.

关键词： Jurats-Switched reluctance motor torque ripple minimization BP neural network Levenberg-Marqnardt algorithm optimum profiling

作者: Yan CAI Chao GAO

作者单位: Tianjin Polytechnic University, China China Tex Mechanical and Electrical Engineering Limited, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）