wupid.m

用bp神经网络实现神经网络pid控制 达到很好的效果

%BP based PID Control 
clear all; 
close all;
xite=0.25; 
alfa=0.05; 
IN=4;m=5;Out=3; %NN Structure 
wi=0.050*rands(m,IN); 
wi1=wi;wi2=wi; 
wo=0.050*rands(Out,m);
wo1=wo;wo2=wo; 
du1=0;
u1=0;
y1=0;
Oh=zeros(m,1); %Output from NN middle layer 
I=Oh; %Input to NN middle layer 
e2=0; 
e1=0; 
 
ts=0.001; 

for k=1:1:100
    
r(k)=1.0; 
time(k)=k*ts; 
%Unlinear model 
a(k)=1.2*(1-0.8*exp(-0.1*k)); 
y(k)=a(k)*y1/(1+y1^2)+u1; 
 
e(k)=r(k)-y(k);xi=[r(k),y(k),e(k),u1]; 
 
 
 
x(1)=e(k)-e1; 
x(2)=e(k); 
x(3)=e(k)-2*e1+e2; 
 
epid=[x(1);x(2);x(3)]; 
I=xi*wi'; 
for j=1:1:m
Oh(j)=(exp(I(j))-exp(-I(j)))/(exp(I(j))+exp(-I(j))); %Middle Layer 
end 
K=wo*Oh;  %Output Layer 
for l=1:1:Out 
K(l)=exp(K(l))/(exp(K(l))+exp(-K(l))); %Getting kp,ki,kd 
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)=u1+du(k); 


 
dyu(k)=sign((y(k)-y1)/(du(k)-du1+0.0001)); 
 
%Output layer 
for j=1:1:Out 
dK(j)=2/(exp(K(j))+exp(-K(j)))^2; 
end 
for l=1:1:Out 
delta3(l)=e(k)*dyu(k)*epid(l)*dK(l); 
end 
 
for l=1:1:Out 
   for i=1:1:m
   dwo=xite*delta3(l)*Oh(i)-alfa*(wo1-wo2); 
   end 
end 
wo=wo1+dwo-alfa*(wo1-wo2); 

%Hidden layer 
for i=1:1:m
dO(i)=4/(exp(I(i))+exp(-I(i)))^2; 
end 
segma=delta3*wo; 
for i=1:1:m 
delta2(i)=dO(i)*segma(i); 
end 
 
dwi=xite*delta2'*xi; 
wi=wi1+dwi-alfa*(wi1-wi2); 
 
%Parameters Update 
du1=du(k);u1=u(k); 
y1=y(k); 

wo2=wo1; 
wo1=wo; 
 
wi2=wi1; 
wi1=wi; 
e2=e1; 
e=e(k); 
end 
 
figure(1);
subplot(311);
plot(time,r,'r',time,y,'b'); 
xlabel('Time（k*ts）');ylabel('输入r输出y'); 
subplot(312);
plot(time,e,'r'); 
xlabel('Time（k*ts）');ylabel('误差e（k）'); 
subplot(313);
plot(time,u,'g'); 
xlabel('Time（k*ts）');ylabel('控制量U'); 
figure(2);
subplot(311); 
plot(time,kp,'r'); 
xlabel('Time（k*ts）');ylabel('kp'); 
subplot(312); 
plot(time,ki,'g'); 
xlabel('Time（k*ts）');ylabel('ki'); 
subplot(313); 
plot(time,kd,'b'); 
xlabel('Time（k*ts）');ylabel('kd');