基于BP神经网络PID的移栽机双闭环调速系统

Double Closed Loop Speed Regulating System of Transplanter Based on BP Neural Network PID

为保证提高移栽机机械手行走速度的同时不失鲁棒性,选用直流电机作为驱动电机,并根据其动作时电流反馈、转速反馈、滤波、整流等多环节的传递函数简化模型设计了基于电流环和转速环的双闭环调速系统模型。引入BP神经网络自学习的控制策略,其中选取BP神经网络PID控制器取代转速环中的PI环节,并在MatLab中采用S函数嵌套控制器模块的方式搭建了基于直流电机双闭环系统仿真模型。结果表明:比较加入BP神经网络算法优化前后的双闭环调速系统响应曲线,优化后的模型超调量由4.3%降为0,过渡时间由2s以上缩短至2s,电机调速系统稳定性和鲁棒性得到提高,整排机械手启动时间更短、速度更快且能准确抓取目标。

In order to improve the moving speed of the transplanter manipulator without loss of robustness,the dc motor is selected as the driving motor,and the dual closed-loop speed control system model based on the current loop and the speed loop is designed according to the simplified transfer function model of the current feedback loop,the speed feedback loop,the filtering loop,the rectification loop and so on Introduction of BP neural network self-learning control strategy,including selecting the BP neural network PID controller to replace stages in PI speed loop,and using S function in MATLAB nested controller module way of double closed-loop system simulation model is built based on dc motor,the simulation results show that:compared to join the BP neural network algorithm is a double closed loop speed regulation system response curves before and after optimization,the optimized model of overshoot amount from 4.3%to 0 transition time reduced from above 2S to 2S,makes the motor speed control system stability and robustness,the whole row of manipulator start time shorter faster at the same time also can grab.