电机与控制应用2011，Vol.38Issue(3)：17-22,6.
基于在线模糊神经网络建模的开关磁阻电机高性能转矩控制
High-Performance Torque Control for Switched Reluctance Motor Based on Online Fuzzy Neural Network Modeling
摘要
Abstract
A novel torque control scheme for switched reluctance motor( SRM ) to reduce its torque ripple was proposed.Firstly, an adaptive neural fuzzy inference system(ANFIS) was designed to learn the nonlinear static position-torque-current characteristic and the flux-linkage characteristic of an SRM offline.Then each phase torque was calculated according to torque share function and the desired phase current waveform was obtained using the ANFIS inverse torque model.Considering the limitation of the offline model and the uncertainties existing in the real-time motor system, the parameters of ANFIS was turned through online supervised learning to improve the accuracy of the inverse torque and the flux-linkage model.Based on the online flux-linkage model, an adaptive sliding-mode current controller was designed to regulate the actual SRM phase winding current to track the desired phase current waveform,thereby reduce the torque ripple of SRM.关键词
开关磁阻电机/转矩控制/模糊神经网络/自适应滑模控制Key words
switched reluctance motor( SRM)/ torque control/ fuzzy neural network/ adaptive sliding mode control分类
信息技术与安全科学引用本文复制引用
姚雪莲,齐瑞云,邓智泉,蔡骏..基于在线模糊神经网络建模的开关磁阻电机高性能转矩控制[J].电机与控制应用,2011,38(3):17-22,6.基金项目
国家自然科学基金(60904042) （60904042）
