神经网络pid控制器MATLAB,BP神经网络PID控制器的Simulink 仿真模型搭建教程
时间: 2023-07-08 22:23:34 浏览: 220
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1. BP神经网络PID控制器的Simulink仿真模型搭建
首先需要安装MATLAB和Simulink软件。
步骤:
1)打开Simulink,新建一个模型。
2)在Simulink库中找到PID控制器模块,将其拖入模型中。
3)在模型中加入BP神经网络模块,将其与PID控制器模块连接。
4)设置BP神经网络模块的输入和输出,可以使用Simulink库中的输入输出模块。
5)设置PID控制器模块的参数,包括比例系数Kp、积分时间Ti和微分时间Td等。
6)设置仿真时间和仿真步长,开始仿真。
2. 神经网络PID控制器MATLAB代码
以下是一份简单的神经网络PID控制器的MATLAB代码示例:
```matlab
% 清空工作区
clear all;
close all;
clc;
% 设置PID控制器参数
Kp = 1;
Ti = 1;
Td = 0.1;
% 设置仿真时间和步长
tspan = [0 10];
dt = 0.01;
% 设置初始值
y0 = 0;
e0 = 0;
de0 = 0;
u0 = 0;
% 定义神经网络
net = feedforwardnet([10 5]);
net.trainFcn = 'trainlm';
% 生成训练数据
t_train = 0:dt:2*pi;
y_train = sin(t_train);
u_train = zeros(size(t_train));
for i = 2:length(t_train)
e_train(i) = y_train(i-1) - u_train(i-1);
de_train(i) = (e_train(i) - e_train(i-1)) / dt;
u_train(i) = Kp * (e_train(i) + (1/Ti)*sum(e_train(1:i))*dt + Td*de_train(i));
end
% 训练神经网络
net = train(net,[e_train;de_train],[u_train]);
% 仿真
[t,y] = ode45(@(t,y) pid_neural_net(t,y,net,Kp,Ti,Td),tspan,y0);
% 绘图
plot(t,y);
xlabel('时间');
ylabel('输出');
title('神经网络PID控制器仿真');
% 定义PID控制器函数
function u = pid_neural_net(t,y,net,Kp,Ti,Td)
e = 0 - y;
de = 0 - y;
u = Kp * (e + (1/Ti)*sum(e)*t + Td*de) + net([e;de]);
end
```
以上是一个简单的神经网络PID控制器的MATLAB代码示例,可以根据具体需求进行修改和优化。
希望这些信息能对您有所帮助!
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