chap5_5.m

基于自适应在线遗传算法整定的PID控制,每个采样时间分别对PD参数进行整定

clear all;
close all;

Size=120;
CodeL=2;

MinX(1)=9*ones(1);
MaxX(1)=12*ones(1);
MinX(2)=0.20*ones(1);
MaxX(2)=0.30*ones(1);

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

BsJ=0;
J=0;

x=zeros(1,2);
xi=zeros(1,2);
xk=zeros(1,2);
ts=0.001;

error_1=0;

BestS=zeros(2,1);
for k=1:1:100
    time(k)=k*ts;
    rin(k)=1;
    
    u(k)=BestS(1)*x(1)+BestS(2)*x(2);
    para=u(k);
    tspan=[0 ts];
    
    [tt,xx]=ode45('chap5_5f',tspan,xk,[],para);
    xk=xx(length(xx),:);
    yout(k)=xk(1);
    
    error(k)=rin(k)-yout(k);
    x(1)=error(k);
    x(2)=(error(k)-error_1)/ts;
    
    error_1=error(k);
    
    B=0;
    
    M=1;
    if M==1
        
        G=10;
        for kg=1:1:G
            
            for i=1:1:Size
           Kpidi=Kpid(i,:);
           
           ui(i)=Kpidi(1)*x(1)+Kpidi(2)*x(2);
           
           para=ui(i);
           [tt,xxi]=ode45('chap5_5f',tspan,xk,[],para);
            Y(i)=xxi(length(xxi),1);
             errori(i)=rin(k)-Y(i);
           du(i)=ui(i)-u(k);
           de(i)=(errori(i)-error(k))/ts;
           alfap=0.95;
           betap=0;
           
           if abs(error(k))<=0.50
               betap=0.05;
           end
           
           J=alfap*abs(errori(i))+betap*abs(de(i));
           
           B=J;
           
          if errori(i)<0
              B=B+100*abs(errori(i));
          end
          
          BsJi(i)=B;
     
      
      [OderJi,IndexJi]=sort(BsJi);
BestJ(kg)=OderJi(1);
BJ=BestJ(kg);
Ji=BsJi+1e-10;    %Avoiding deviding zero
 end
   fi=1./Ji;
%  Cm=max(Ji);
%  fi=Cm-Ji;                     
   
   [Oderfi,Indexfi]=sort(fi);    %Arranging fi small to bigger
   Bestfi=Oderfi(Size);          %Let Bestfi=max(fi)
   BestS=Kpid(Indexfi(Size),:);  %Let BestS=E(m), m is the Indexfi belong to max(fi)
   
   
%****** Step 2 : 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 Sizeth and Reproduce firstly  
      for j=1:1:fi_S(i)  
       TempE(k,:)=Kpid(Indexfi(i),:);      % Select and Reproduce 
         k=k+1;                            % k is used to reproduce
      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 Operation **************
Pm=0.20-[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

kph(k)=BestS(1);
kdh(k)=BestS(2);
BestS
end
end

figure(1);
plot(time,rin,'r',time,yout,'b');
xlabel('Time(s)');Ylabel('rin,yout');

figure(2);
plot(time,u,'r');
xlabel('Time(s)');Ylabel('u');

if M==1
    figure(3);
    plot(time,kph,'r');
    xlabel('Time(s)');Ylabel('kp change');
figure(4);
plot(time,kdh,'r');
xlabel('Time(s)');Ylabel('kd change');
end