基于自适应模糊神经网络的异步电动机SVM-DTC控制

SVM-DTC Control of Induction Motor based on Adaptive Fuzzy Neural Network

本文提出一种基于自适应模糊神经网络(ANFIS)异步电动机直接转矩(SVM-DTC)控制系统方法。该方法充分利用了ANFIS具有处理不确定、非线性问题的能力以及自适应、自学习能力的特点,有效克服了异步电动机非线性多变量耦合的缺陷,提高了系统的鲁棒性和动态性能。经过Matlab/Simulink仿真验证,该方法可有效抑制磁链和转矩脉动,具有比传统直接转矩控制更好的控制效果。

Amethod that basing on Adaptive neuro-fuzzy network（ANFIS） in SVM-DTC Control system of Induction Motoris proposedin this paper. Making the best use of ANFIS＇ ability of dealing with uncertain nonlinear object and the ability of self-adaptation and self-study, this method could overcome the defects of nonlinear and multi-variable coupling in the system of asynchronous motor effectively. It can also improve the robustness and dynamic performance of the system. The simulation results show that this method could reduce the flux and the torque ripple effectively and has a good performance than traditional DTC system.