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
figure(1);
plot(time,rin,'r',time,yout,'b'); xlabel('time(s)');ylabel('rin,yout'); figure(2);
plot(time,error,'r');
xlabel('time(s)');ylabel('error'); figure(3);
plot(time,u,'r');
xlabel('time(s)');ylabel('u');
figure(4);
subplot(311);
plot(time,kp,'r');
xlabel('time(s)');ylabel('kp'); subplot(312);
plot(time,ki,'g');
xlabel('time(s)');ylabel('ki'); subplot(313);
plot(time,kd,'b');
xlabel('time(s)');ylabel('kd');。