一种基于神经网络的电机控制算法

A Motor Control Algorithm Based on Neural Network

由于电机负载特性以及其他非线性因素影响,传统PID控制很难获得满意的效果,为此提出了一种基于神经网络的电机控制算法。通过机器学习训练神经网络,使神经网络模型获得一定的自适应能力,采用梯度下降反向传播算法获得电机最优控制参数。仿真和实验结果表明:神经网络电机控制算法能够有效抑制超调,缩短调整时间,提高系统响应速度,克服被控对象的不确定性以及随机扰动的影响,控制效果明显提升。

Due to the load characteristics of the motor and other nonlinear factors,it is difficult for traditional PID to obtain satisfactory control effect.A motor neural network control algorithm was put forward.The network was trained with machine learning algorithm and neural network model obtain certain adaptive ability.The gradient descent back propagation algorithm is used to obtain the optimal control parameters of the motor.Simulation and experimental results show that it can effectively suppress overshoot,shorten adjustment time,improve system response speed and overcome the uncertainty of controlled object and the influence of random disturbance.The control effect was significantly improved.