直流无刷电机神经网络直接转矩控制

Neural Network Control on Direct Torque Control for Brushless DC Motor

为简化无刷直流电机控制系统的结构,同时减小直流无刷电机的转矩脉动,将直接转矩控制和神经网络控制相结合,应用于无刷直流电机控制系统中。直接转矩控制省掉了复杂的矢量变换。根据转矩偏差和转矩偏差的变化率,运用神经网络的非线性控制特性,调节电压矢量作用时间,减小转矩脉动。实验结果表明,神经网络直接转矩控制具有良好的动态特性和简单的结构,实现了对电流和转矩的有效控制,转子相电流波动和转矩脉动减少50%左右,提高了控制精度。

In order to simplify the control system's structure and reduce the torque ripple restraint,direct torque control(DTC) and neural network control strategy were combined in the control system of the brushless DC motor's control system.The DTC control can leave out complex vector quantity transfer.According to the error between torque and its reference value and the error variance ratio,using the non-linearity control characteristics of the neural network, adjusted the active time of the space voltage and reduce the torque ripple.The experimental result prove that the neural networks DTC method has good dynamic characteristics and simple structure,makes effective control on torque and corrent, and the fluctuation of the phase current and the torque decrease by about 50%.It enhances the control precision of the BLDCM.