Hopfield自适应异步电动机的直接转矩控制

Direct Torque Control of Adaptive Asynchronous Motor Based on Hopfield Neural Network

传统的异步电动机直接转矩控制方法中,电动机低速稳态运行时的电磁转矩、定子磁链和定子电流脉动大,严重影响了整个电动机直接转矩控制系统的性能.为此,文中基于Hopfield神经网络理论和异步电动机动态数学模型,提出了基于Hopfield神经网络的改进异步电动机直接转矩控制方法,有效地降低了电磁转矩、定子磁链和定子电流的波动,达到了改善调速系统低速性能的目的.在此基础上,文中还进行了理论建模和仿真计算,仿真结果表明该方法具有良好的鲁棒性.

In order to enhance the performances of the traditional direct torque control system of the asynchronous motor that are restricted by the great ripples of the electromagnetic torque, the stator flux and the stator current at a low steady motor speed, an improved direct torque control method is proposed based on the I4opfield neural network and the dynamic mathematical model of the asynchronous motor. It is found that the proposed method not only effectively reduces the ripples of the electromagnetic torque, the stator flux and the stator current but also enhances the low-speed performance of the speed control system. Modeling and Simulation results indicate that the proposed method is of excellent robustness.