MATLAB基于BP神经网络PID控制程序.pdf

MATLAB基于BP神经网络PID控制程序

>> %BP based PID Control

clear all;

close all;

xite=0.20; %学习速率

alfa=0.01; %惯性因子

IN=4;H=5;Out=3; %NN Structure

wi=[-0.6394 -0.2696 -0.3756 -0.7023;

-0.8603 -0.XXXX -0.5024 -0.2596;

-1.0749 0.5543 -1.6820 -0.5437;

-0.3625 -0.0724 -0.6463 -0.2859;

0.1425 0.0279 -0.5406 -0.7660];

%wi=0.50*rands(H,IN); %隐含层加权系数wi初始化

wi_1=wi;wi_2=wi;wi_3=wi;

wo=[0.7576 0.2616 0.5820 -0.1416 -0.1325;

-0.1146 0.2949 0.8352 0.2205 0.4508;

0.7201 0.4566 0.7672 0.4962 0.3632];

%wo=0.50*rands(Out,H); %输出层加权系数wo初始化

wo_1=wo;wo_2=wo;wo_3=wo;

ts=20; %采样周期取值

x=[0,0,0]; %比例，积分，微分赋初值

u_1=0;u_2=0;u_3=0;u_4=0;u_5=0;

y_1=0;y_2=0;y_3=0;

Oh=zeros(H,1); %Output from NN middle layer 隐含层的输出

I=Oh; %Input to NN middle layer 隐含层输入

error_2=0;

error_1=0;

for k=1:1:500 %仿真开始，共500步

time(k)=k*ts;

rin(k)=1.0;

%Delay plant

sys=tf(1.2,[208 1],'inputdelay',80); %建立被控对象传递函数? dsys=c2d(sys,ts,'zoh'); %把传递函数离散化? [num,den]=tfdata(dsys,'v'); %离散化后提取分子、分母

yout(k)=-den(2)*y_1+num(2)*u_5;

error(k)=rin(k)-yout(k);

xi=[rin(k),yout(k),error(k),1];

%经典增量式数字PID 的控制算式为：

()(1)(()(1))()(()2(1)(2))p i d u k u k k e k e k ke k k e k e k e k =−+−−++−−+− BP 神经网络PID 的控制算式为：

()()()333123()(1)(()(1))()(()2(1)(2))u k u k o e k e k o e k o e k e k e k =−+−−++−−+− x(1)=error(k)-error_1; %比例输出

x(2)=error(k); %积分输出

x(3)=error(k)-2*error_1+error_2; %微分输出 epid=[x(1);x(2);x(3)];

I=xi*wi';% 隐含层的输入，即：输入层输入*权值 for j=1:1:H Oh(j)=(exp(I(j))-exp(-I(j)))/(exp(I(j))+exp(-I(j))); %Middle Layer 在激活函数作用下隐含层的输出

end

K=wo*Oh; %Output Layer 输出层的输入，即：隐含层的输出*权值 for l=1:1:Out

K(l)=exp(K(l))/(exp(K(l))+exp(-K(l))); %Getting kp,ki,kd 输出层的输出，即三个pid 控制器的参数

end kp(k)=K(1);ki(k)=K(2);kd(k)=K(3);

Kpid=[kp(k),ki(k),kd(k)];

du(k)=Kpid*epid;

u(k)=u_1+du(k);

if u(k)>=10 % Restricting the output of controller 控制器饱和环节 u(k)=10; end

if u(k)<=-10

u(k)=-10;

end

%以下为权值wi 、wo 的在线调整，参考刘金琨的《先进PID 控制》

dyu(k)=sign((yout(k)-y_1)/(u(k)-u_1+0.0000001));

%Output layer 输出层

for j=1:1:Out

dK(j)=2/(exp(K(j))+exp(-K(j)))^2;

end

for l=1:1:Out

delta3(l)=error(k)*dyu(k)*epid(l)*dK(l); end

for l=1:1:Out

for i=1:1:H

d_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2); end

end

wo=wo_1+d_wo+alfa*(wo_1-wo_2);

%Hidden layer

for i=1:1:H

dO(i)=4/(exp(I(i))+exp(-I(i)))^2;

end

segma=delta3*wo;

for i=1:1:H

delta2(i)=dO(i)*segma(i);

end

d_wi=xite*delta2'*xi;

wi=wi_1+d_wi+alfa*(wi_1-wi_2);

%Parameters Update 参数更新

u_5=u_4;u_4=u_3;u_3=u_2;u_2=u_1;u_1=u(k); y_2=y_1;y_1=yout(k);

wo_3=wo_2;

wo_2=wo_1;

wo_1=wo;

wi_3=wi_2;

wi_2=wi_1;

wi_1=wi;

error_2=error_1;

error_1=error(k);

end

%仿真结束，绘图