BP神经网络在双伺服同步运动系统中的应用

Application of BP Neural Network in Double Servo Synchronous Motor System

针对传统的PID调节器不能解决双伺服同步运动系统中经常出现的超调和滞后问题,提出一种将传统PID和BP神经网络的PID调节器相结合的方式来控制两伺服电机轴的运动。其中,传统PID算法用于系统正常运行时的控制,而BP神经网络的PID算法用于调试过程中修改位置调整器和速度调节器的参数。该方案能实现轴B准确地跟踪轴A的速度和轨迹而运动。

In the double servo synchronous motor system, the current speed often surpasses or lags the given speed, however the traditional PID algorithm can＇t solve this problem. Therefore, introduce a new PID regulator which combines the traditional PID with BP neural network＇s PID algorithm, it can control the movement of two servo motors. The traditional PID is used for controlling the two axles during normal operation. However, BP neural network PID algorithm is used to modify the parameters of the position regulator and parameters of the speed regulator in the process of debugging. Through this method, we can achieve the axis B track the speed and orbit of the axis A accurately.