nnpiddistub2.m

神经网络抗扰例子

%BP based PID Control
clear all;
close all;

xite=0.28;
alfa=0.04;

Kp0=1.2;
Ki0=.002;
Kd0=0.5;


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

wi=[0.2948    0.4797   -0.3635    0.1614;
    0.4568   -0.2286   -0.4882   -0.2156;
    0.0226   -0.2477    0.3939   -0.0308;
    0.3801    0.3757   -0.3009   -0.4352;
   -0.3270    0.2373   -0.2013    0.4883];
%wi=0.50*rands(H,IN);
wi_1=wi;wi_2=wi;wi_3=wi;
wo=[0.0828   -0.1660    0.0798    0.1405    0.2833;
   -0.0765   -0.0671    0.2604   -0.2909    0.1808;
    0.0155   -0.2741    0.0298   -0.1202   -0.0389];
%wo=0.50*rands(Out,H);
wo_1=wo;wo_2=wo;wo_3=wo;

x=[0,0,0];
du_1=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=0.1;
sys=tf(0.1703,[3.013 4.373 2.521],'inputdelay',8);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');
%sys1=tf([0.3013,0.4373,0.4264],[3.013 4.373 2.521],'inputdelay',8);
%dsys1=c2d(sys1,ts,'z');
%[num1,den1]=tfdata(dsys1,'v');

for k=1:1:1000
time(k)=k*ts;

rin(k)=0.7+0.6*rand(1);
%Unlinear model
%if(k<180)
%    yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2;
%else
%    yout(k)=-den1(2)*y_1-den1(3)*y_2+num1(2)*u_1+num1(3)*u_2;
%end
%if(k<500)
    yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2;
    %else
 %   yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1*1.3+num(3)*u_2;
 %end
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
Vkp(k)=K(1);Vki(k)=K(2);Vkd(k)=K(3);
kp(k)=Kp0+Vkp(k);
ki(k)=Ki0+Vki(k);
kd(k)=Kd0+Vkd(k);
Kpid=[kp(k),ki(k),kd(k)];

du(k)=Kpid*epid;
u(k)=u_1+du(k);

dyu(k)=sign((yout(k)-y_1)/(du(k)-du_1+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)=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
du_1=du(k);
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
Kpid
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');