Neural PID Control System Code - Real-Time Rendering: Computer Graphics with Control Engineering - page 168

elseif k< = 10000
rin(k) = 200;
else
rin(k) = 50;
end
try
u(k) = u_1+K*w*x; %Control law
catch
u(k) = 0;
end
% Plant model
yout(k) = b(1) * u(k) + b(2) * u_1 + b(3) * u_2 + b(4) * u_3 - a(2)
* y_1 - a(3) * y_2 - a(4) * y_3;
% Error
error(k) = rin(k)-yout(k);
%Adjusting Weight Value by hebb learning algorithm
M = 4;
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)];
error_2 = error_1;
error_1 = error(k);
u_3 = u_2;u_2 = u_1;u_1 = u(k);