文件包含基于Matlab编写的一些先进PID控制程序

				%GA(Generic Algorithm) Program to optimize 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=100;
				BsJ=0;
				
				%*************** Start Running ***************
				for kg=1:1:G
				    time(kg)=kg;
				
				%****** Step 1 : Evaluate BestJ ******
				for i=1:1:Size
				Kpidi=Kpid(i,:);
				    
				[Kpidi,BsJ]=chap5_3f(Kpidi,BsJ);
				
				BsJi(i)=BsJ;
				end
				 
				[OderJi,IndexJi]=sort(BsJi);
				BestJ(kg)=OderJi(1);
				BJ=BestJ(kg);
				Ji=BsJi+1e-10;    %Avoiding deviding zero
				
				   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)
				   
				   kg   
				   BJ
				   BestS
				%****** 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=0.10-[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');