bppid.m

MATLAB平台下的基于人工神经网络的非线性控制系统PID参数整定代码

%基于bp神经网络pid控制程序


%BP based PID Control 
clear all; 
close all; 

xite=0.25; 
alfa=0.05; 

S=2; %Signal type

IN=4;H=5;Out=3; %NN Structure 
if S==1 %Step Signal 
wi=[-0.6394 -0.2696 -0.3756 -0.7023; 
-0.8603 -0.2013 -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); 
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); 
wo_1=wo;wo_2=wo;wo_3=wo; 
end 

if S==2 %Sine Signal 
wi=[-0.2846 0.2193 -0.5097 -1.0668; 
-0.7484 -0.1210 -0.4708 0.0988; 
-0.7176 0.8297 -1.6000 0.2049; 
-0.0858 0.1925 -0.6346 0.0347; 
0.4358 0.2369 -0.4564 -0.1324]; 
%wi=0.50*rands(H,IN); 
wi_1=wi;wi_2=wi;wi_3=wi; 
wo=[1.0438 0.5478 0.8682 0.1446 0.1537; 
0.1716 0.5811 1.1214 0.5067 0.7370; 
1.0063 0.7428 1.0534 0.7824 0.6494]; 
%wo=0.50*rands(Out,H); 
wo_1=wo;wo_2=wo;wo_3=wo; 
end 

x=[0,0,0]; 
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
I=Oh; %Input to NN middle layer 
error_2=0; 
error_1=0; 

ts=0.001; 
for k=1:1:6000 
time(k)=k*ts; 

if S==1 
rin(k)=1.0; 
elseif S==2 
rin(k)=sin(1*2*pi*k*ts); 
end 

%Unlinear model 
a(k)=1.2*(1-0.8*exp(-0.1*k));
yout(k)=a(k)*y_1/(1+y_1^2)+u_1;

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

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

x(1)=error(k)-error_1; 
x(2)=error(k); 
x(3)=error(k)-2*error_1+error_2; 

epid=[x(1);x(2);x(3)]; 
I=xi*wi'; 
for j=1:1:H 
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)=u_1+du(k); 
if u(k)>=10 % Restricting the output of controller 
u(k)=10; 
end 
if u(k)<=-10 
u(k)=-10; 
end 

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

%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)=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 
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 
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');