【摘要】 提出了一种改进的通过使用CMAC(CerebellarModelArticulationController———脑模型关节控制器 )神经网络控制变磁阻式电动机 (SRM)转矩脉动减小的算法。该算法在保持原LMS算法优点的基础上 ,能确保电机以低速运转时最小化转矩脉动、铜耗及电机以高速运转时最小化转矩脉动、铜耗、电流的变化率均产生最佳的电流曲线。更多还原

【Abstract】 This paper presents a improved learning control algorithm in switched reluctance motors(SRM) for torque ripple reduction with a CMAC neural network.Based on maintaining the advantage of the original agorithm the approach allows the generation of optimal current profiles in terms of minimizing torque ripple and copper loss as the motor operates at low speeds,and of minimizing torque ripple,copper loss and rate of change of current as the motor runs at high speeds.更多还原