% %Single Neural Adaptive PID Controller
clear all;
close all;
x=[0,0,0]';
xiteP=3.7;
xiteI=0.75;
xiteD=1.5;
%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;
sys=tf(1770,[1,60,1770]);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v')
for k=1:1:1000
time(k)=k*ts;
rin(k)=1.0;
yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2;
%yout(k)=0.368*y_1+0.26*y_2+0.1*u_1+0.632*u_2;
error(k)=rin(k)-yout(k);
%Adjusting Weight Value by hebb learning algorithm
%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;
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)];
%xite=[xiteP,xiteI,xiteD];
%x=[x(1);x(2);x(3)];
u(k)=u_1+K*w*x; %Control law
if u(k)>10
u(k)=10;
end
if u(k)<-10
u(k)=-10;
end
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=yout(k);
wkp_1=wkp(k);
wkd_1=wkd(k);
wki_1=wki(k);
end
figure(1);
plot(time,rin,'b',time,yout,'r');
xlabel('时间/s');ylabel('下水箱液位值/mm');
%figure(2);
%5plot(time,error,'r');
%xlabel('time(s)');ylabel('error');
%figure(3);
%plot(time,u,'r');
%xlabel('time(s)');ylabel('u');
%plot(time,w,'r');
%xlabel('time(s)');ylabel('kp');
