bb.m

基于bp神经网络的pid控制算法

%BP PID;
clear all;
close all;

xite=0.20;
alfa=0.05;

S=2;%Signal type

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

if S==1 %Step Signal
wi =[ 0.2615    0.9062    0.3323   -0.6475;
     -0.8113   -0.5003    0.9123    0.5867;
     -0.9501   -0.6954    0.3195   -0.1660;
     -0.1025    0.7396    0.7274    0.7610;
      0.6656   -0.5531   -1.0254    0.3957];
    %wi=2*rand(H,IN)-ones(5,4);
 wi_1=wi;wi_2=wi;wi_3=wi;
wo =[ 0.4358   -0.6276    0.7731   -0.0173   -0.1144
     -0.2466   -0.5566    0.0417   -0.0241    0.8644
     -0.6068   -0.0939    0.6930   -0.0375   -0.9277];
 % wo=2*rand(Out,H)-ones(3,5);
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=0.5*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];
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;
error_2=0;
error_1=0;

ts=0.001;
for k=1:1:3000
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);
sum(k)=abs(error(k))+sum(k-1);
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);

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(1)=error(k)*dyu(k)*epid(l)*dk(l);
end

for l=1:1:Out
    for i=1:1:H
        d_wo=xite*delta3(1)*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
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');
wi
wo
sum(k)