ga.m

matlab pid算法的详细算法 还不错

% GA (generic Algorithm)program to 0ptimize PID Parameters

clear all;
close all;
global rin yout timef

Size=30;
CodeL=3;

MinX(1)=zeros(1);
MaxX(1)=20*ones(1);
MinX(2)=zeros(1);
MaxX(2)=1.0*ones(1);
MinX(3)=zeros(1);
MaxX(3)=1.0*ones(1);

Kpid(:,1)=MinX(1)+(MaxX(1)-MinX(1))*rand(Size,1);
Kpid(:,2)=MinX(2)+(MaxX(2)-MinX(2))*rand(Size,1);
Kpid(:,3)=MinX(3)+(MaxX(3)-MinX(3))*rand(Size,1);

G=10;
BsJ=0;

%*************start running*********
for kg=1:1:G
    time(kg)=kg;
    
    %******step1:evaluate best J******
    for i=1:1:Size
        Kpidi=Kpid(i,:);
        
        [Kpidi,BsJ]=pid_gaf(Kpidi,BsJ);
        
       BsJi(i)=BsJ;
   end
   
   [OderJi,IndexJi]=sort(BsJi);
   BestJ(kg)=OderJi(1);
   
  BJ=BestJ(kg);
  Ji=BsJi+1e-10; %aboiding debiding zero
  
  fi=1./Ji;
  %cm=max(Ji);fi=cm=Ji;
  
  [Oderfi,Indexfi]=sort(fi);
  Bestfi=Oderfi(Size);
  BestS=Kpid(Indexfi(Size),:);
  max(fi);

  kg;
  BJ;
  BestS;
  
  
  %*******step2:select and reproduct operation*****
  
  fi_sum=sum(fi);
  fi_Size=(Oderfi/fi_sum)*Size;
  
  fi_S=floor(fi_Size);%selecting bigger fi value
  r=Size-sum(fi_S);
  
  Rest=fi_Size-fi_S;
  [RestValue,Index]=sort(Rest);
    
  for i=Size:-1:Size-r+1
      fi_S(Index(i))=fi_S(Index(i))+1; %adding rest to equal size
  end
  
  k=1;
  for i=Size:-1:1%select the size and reproduce firstly
      for j=1:1:fi_S(i)
          TempE(k,:)=Kpid(Indexfi(i),:);
         k=k+1;
     end
 end
 
 %********step 3:crossover operation*******
 Pc=0.90;
 for i=1:2:(Size-1)
     temp=rand;
     if Pc>temp   %crossover condition
         alfa=rand;
         TempE(i,:)=alfa*Kpid(i+1,:)+(1-alfa)*Kpid(i,:);
          TempE(i+1,:)=alfa*Kpid(i,:)+(1-alfa)*Kpid(i+1,:);
      end
  end
  TempE(Size,:)+BestS;
  Kpid=TempE;
  
  %*******step 4:mutation opertion*******
  
  Pm=0.10-[1:1:Size]*(0.01)/Size; %bigger fi,smaller Pm
  Pm_rand=rand(Size,CodeL);
  Mean=(MaxX+MinX)/2;
  Dif=(MaxX-MinX);
  
  
  for i=1:1:Size
      for j=1:1:CodeL
          if Pm(i)>Pm_rand(i,j)  %Mutation condition 
              TempE(i,j)=Mean(j)+Dif(j)*(rand-0.5);
          end
      end
  end
      
      %guarantee TempE(Size,:)belong to the best individual
      TempE(Size,:)=BestS;
      Kpid=TempE;
  end
  
  
  Bestfi
  BestS
  Best_J=BestJ(G)
  figure(1);
  plot(time,BestJ);
  xlabel('Times');ylabel('Best J');
  figure(2);
  plot(timef,rin,'r',timef,yout,'b');
  xlabel('Time(s)');ylabel('rin,yout');