神经网络预测在无刷直流电机调速中的应用

Application of Neural Network Prediction in Speed Regulation of Brushless DC Motor

针对传统无刷直流电机调速系统中转速控制器参数不易设定、传统控制中依据反馈信号控制当前状态会产生延迟且转速预测精度较低等弊端,提出了一种改进的神经网络预测控制方法。该方法将BP神经网络和预测控制相结合,并在转速预测环节利用曲率圆作为参考模型实现神经网络的训练。通过设置适当的输入层和隐含层,实现了神经网络对误差信号的有效调节,并预测出电机下一时刻的转速,从而有效地减小了控制作用的延迟时间,使非线性控制系统的参数设定具有良好的准确性,并能提高无刷直流电动机的转速预测精度和准确度。仿真结果表明,神经网络预测控制能够在1 000~3 000 r/min的调速范围内,有效改善系统的控制精度、实时性和稳定性。该方法也可应用于轧机、变频空调压缩机等调速控制系统。

The parameters of the traditional speed regulation controller of brushless DC motor are difficult to be set up,and in traditional control,the prediction precision of the speed is low because of the delay caused by the current state is controlled in accordance the feedback signal.To overcome these disadvantages,the improved neural network prediction control method is proposed.With this method,the BP neural network is combined with the prediction control,and in the speed prediction section,the training of the neural network is implemented by using curvature circle as the reference model.Through setting appropriate input layer and hidden layer,effective regulation of the error signal for neural network can be achieved,and the speed of motor at the next moment can be predicted,thus the time delay of the control action is reduced effectively,to obtain excellent accuracy of the parameters set for nonlinear control system,and to enhance the prediction accuracy for speed of DC motor.The simulation results fully verify that the neural network prediction control can effectively improve the control accuracy of system within the range of1 000 ~ 3 000 r/min,and real time performance and stability.The method can also be applied in other speed control systems,such as rolling mills,and compressors of VF air conditioning,etc.