BP神经网络在电动执行器控制中的应用

Application of BP neural network in the control of electric actuator

为提高电动执行器的控制精度、消除电机振荡,满足智能制造领域对电动执行器控制性能越来越高的需求,将人工神经网络应用于电动执行器的控制中,设计一套高精度、高稳定性的电动执行器。该电动执行器将电机的电压、电流、转速、温度作为输入量,利用人工神经网络预估电机停止位置,避免电机振荡,其控制精度由国家标准规定的5%提高到1%。实验结果表明,该电动执行器电路更加简单,能够有效提高系统稳定性和控制精度。

In order to improve the control precision of the electric actuator,eliminate the oscillation of the motor,and meet the increasing demand for the control performance of the electric actuator in the field of intelligent manufacturing,the artificial neural network is applied to the control of the electric actuator,and a set of high precision and high stability electric actuator is designed. The electric actuator uses the voltage,current,speed and temperature of the motor as input,and uses artificial neural network to predict the stop position of the motor,avoids the oscillation of the motor. The control precision is increased to 1% from 5% of the national standard. The experimental results show that the electric actuator circuit is simpler and can effectively improve the system stability and control accuracy.