pid_evl.m

遗传算法对PID参数的优化

%PID_EVL.M
function[Kpidi,BsJ]=pid_gaf(Kpidi,BsJ)
global rin yout timef
ts=0.020;
sys=tf([0.31,1.169],[1,3.215,2.584]);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');
rin=1.0;
u_1=0.0;u_2=0.0;
y_1=0.0;y_2=0.0;
x=[0,0,0]';
B=0;
error_1=0;
tu=1;
s=0;
P=200;
for k=1:1:P
    timef(k)=k*ts;
    r(k)=rin;
    u(k)=Kpidi(1)*x(1)+Kpidi(2)*x(2)+Kpidi(3)*x(3);
    if u(k)>=10
        u(k)=10;
    end
    if u(k)<=-10
       u(k)=-10;
   end
   yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2;
   error(k)=r(k)-yout(k);

   
  %----------------Return of PID parameters-----------
  u_2=u_1;u_1=u(k);
  y_2=y_1;y_1=yout(k);
  x(1)=error(k);                              %Calulating P
  x(2)=(error(k)-error_1)/ts;                 %Calulating D
  x(3)=x(3)+error(k)*ts;                      %Calulating I
  error_2=error_1;
  error_1=error(k);
  if s==0
      if yout(k)>0.95&yout(k)<1.05
         tu=timef(k);
         s=1;
       end
   end
end
for i=1:1:P
    Ji(i)=0.999*abs(error(i))+0.01*u(i)^2*0.1;
    B=B+Ji(i);
    if i>1
        erry(i)=yout(i)-yout(i-1);
        if erry(i)<0
            B=B+100*abs(erry(i));
        end
    end
end
BsJ=B+0.2*tu*10;