【摘要】 永磁无刷直流电机位置传感器和转矩波动的存在限制了它的应用范围.为扩大无刷直流电机在精度较高的伺服系统中的应用,提出在实现无位置传感器控制的同时,减少转矩波动的新策略.无刷直流电机转子位置与电机的电压、电流之间以及电机电流与转子位置、转矩之间都存在着一定的非线性对应关系.通过对两个径向基函数(RBF)神经网络按自适应训练算法进行训练,训练后的两个RBF神经网络分别实现了无刷直流电机转子位置和最大转矩运行时参考电流的在线估计.根据估计的参考电流对绕组的实际电流进行调节,最大限度地抑制了因电流波形不理想引起的转矩波动,为无刷直流电机在高性能伺服系统中的应用提供了保证.实验结果表明了此控制策略的有效性.更多还原

【Abstract】 For the being of the torque ripple and the position sensors, the application of brushless DC motors is limited. To enlarge the applications in high accurate servos, a novel method for torque ripple minimization based on radical basis function(RBF) neural network for a sensorless brushless DC motor is proposed.The analysis shows that there is a nonlinear relation between the rotor position and the currents and voltages of the motor, and the currents of the motor without torque ripples are the function of the torque and the rotor position. Two RBF neural networks are trained offline with an adaptive algorithm, which is proposed to obtain the simplest and tightest structure. One of the trained networks is used to realize the function between the voltages and phase currents of the motor, and the other one is for the estimation of the reference currents with a desired torque.Then the real currents in the armatures are adjusted according to the reference values, therefore the torque ripple generated by the non-ideal current waveforms is minimized for a brushless DC motor without position sensors. The experimental results show the efficiency of the proposed method.更多还原