%Single Neural Adaptive PID Controller
clear all;
close all;
x=[0,0,0]';
xiteP=0.40;
xiteI=0.35;
xiteD=0.40;
%Initilizing kp,ki and kd
wkp_1=0.10;
wki_1=0.10;
wkd_1=0.10;
%wkp_1=rand;
%wki_1=rand;
%wkd_1=rand;
error_1=0;
error_2=0;
y_1=0;y_2=0;y_3=0;
u_1=0;u_2=0;u_3=0;
ts=0.001;
for k=1:1:1000
time(k)=k*ts;
yd(k)=0.5*sign(sin(2*2*pi*k*ts));
y(k)=0.368*y_1+0.26*y_2+0.1*u_1+0.632*u_2;
error(k)=yd(k)-y(k);
%Adjusting Weight Value by hebb learning algorithm
M=1;
if M==1 %No Supervised Heb learning algorithm
wkp(k)=wkp_1+xiteP*u_1*x(1); %P
wki(k)=wki_1+xiteI*u_1*x(2); %I
wkd(k)=wkd_1+xiteD*u_1*x(3); %D
K=0.06;
elseif M==2 %Supervised Delta learning algorithm
wkp(k)=wkp_1+xiteP*error(k)*u_1; %P
wki(k)=wki_1+xiteI*error(k)*u_1; %I
wkd(k)=wkd_1+xiteD*error(k)*u_1; %D
K=0.12;
elseif M==3 %Supervised Heb learning algorithm
wkp(k)=wkp_1+xiteP*error(k)*u_1*x(1); %P
wki(k)=wki_1+xiteI*error(k)*u_1*x(2); %I
wkd(k)=wkd_1+xiteD*error(k)*u_1*x(3); %D
K=0.12;
elseif M==4 %Improved Heb learning algorithm
wkp(k)=wkp_1+xiteP*error(k)*u_1*(2*error(k)-error_1);
wki(k)=wki_1+xiteI*error(k)*u_1*(2*error(k)-error_1);
wkd(k)=wkd_1+xiteD*error(k)*u_1*(2*error(k)-error_1);
K=0.12;
end
x(1)=error(k)-error_1; %P
x(2)=error(k); %I
x(3)=error(k)-2*error_1+error_2; %D
wadd(k)=abs(wkp(k))+abs(wki(k))+abs(wkd(k));
w11(k)=wkp(k)/wadd(k);
w22(k)=wki(k)/wadd(k);
w33(k)=wkd(k)/wadd(k);
w=[w11(k),w22(k),w33(k)];
u(k)=u_1+K*w*x; %Control law
error_2=error_1;
error_1=error(k);
u_3=u_2;u_2=u_1;u_1=u(k);
y_3=y_2;y_2=y_1;y_1=y(k);
wkp_1=wkp(k);
wkd_1=wkd(k);
wki_1=wki(k);
end
figure(1);
plot(time,yd,'r',time,y,'k:','linewidth',2);
xlabel('time(s)');ylabel('yd,y');
legend('ideal position','position tracking');
figure(2);
plot(time,u,'r','linewidth',2);
xlabel('time(s)');ylabel('Control input');
