基于二进制编码遗传算法的PID整定的仿真程序_matlab

%GA(Generic Algorithm) Program to optimize Parameters of PID clear all; close all; global rin yout timef % define the global variable G=20; % 最大迭代次数 Size=30; % 样本个数 CodeL=10; %二进制的位数 MinX(1)=zeros(1); %Kp的取值范围的最小值：0 MaxX(1)=20*ones(1); %Kp的取值范围的最大值：20 MinX(2)=zeros(1); %Ki的取值范围下限：0 MaxX(2)=1.0*ones(1); %Ki的取值范围上限：1 MinX(3)=zeros(1); %Kd的取值范围下限：0 MaxX(3)=1.0*ones(1); %Kd的取值范围上限：1 E=round(rand(Size,3*CodeL)); %初始编码，采用随机数函数和round函数生成30*30的编码矩阵 BsJ=0; for kg=1:1:G time(kg)=kg; for s=1:1:Size m=E(s,:); y1=0;y2=0;y3=0; m1=m(1:1:CodeL); for i=1:1:CodeL y1=y1+m1(i)*2^(i-1); end Kpid(s,1)=(MaxX(1)-MinX(1))*y1/1023+MinX(1); %第S个体的Kp的初始值 m2=m(CodeL+1:1:2*CodeL); for i=1:1:CodeL y2=y2+m2(i)*2^(i-1); end Kpid(s,2)=(MaxX(2)-MinX(2))*y2/1023+MinX(2); %第S个体的Ki的初始值 m3=m(2*CodeL+1:1:3*CodeL); for i=1:1:CodeL y3=y3+m3(i)*2^(i-1); end Kpid(s,3)=(MaxX(3)-MinX(3))*y3/1023+MinX(3); %第S个体的Kd的初始值 %****** Step 1 : Evaluate BestJ ****** Kpidi=Kpid(s,:); %暂存S个体的初始值 [Kpidi,BsJ]=chap5_3f(Kpidi,BsJ); %得到第S个体的适应度指标 BsJi(s)=BsJ; %保存第S个体的适应度指标值 end [OderJi,IndexJi]=sort(BsJi); %对群体中个体适应度值“升序”排序，记录下标值IndexJi。 BestJ(kg)=OderJi(1); BJ=BestJ(kg); Ji=BsJi+1e-10; fi=1./Ji; % Cm=max(Ji); % fi=Cm-Ji; %Avoiding deviding zero [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) Bestfi=Oderfi(Size); % Let Bestfi=max(fi) BestS=E(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 kk=1; for i=1:1:Size for j=1:1:fi_S(i) %Select and Reproduce TempE(kk,:)=E(Indexfi(i),:); kk=kk+1; %kk is used to reproduce end end %************ Step 3 : Crossover Operation ************ pc=0.60; %交叉概率 n=ceil(20*rand); %产生一随机整数 for i=1:2:(Size-1) temp=rand; if pc>temp %Crossover Condition for j=n:1:20 TempE(i,j)=E(i+1,j); TempE(i+1,j)=E(i,j); end end end TempE(Size,:)=BestS; E=TempE; %************ Step 4: Mutation Operation ************** %pm=0.001; pm=0.001-[1:1:Size]*(0.001)/Size; %Bigger fi, smaller pm %pm=0.0; %No mutation %pm=0.1; %Big mutation for i=1:1:Size for j=1:1:3*CodeL temp=rand; if pm>temp %Mutation Condition if TempE(i,j)==0 TempE(i,j)=1; else TempE(i,j)=0; end end end end %Guarantee TempE(Size,:) belong to the best individual TempE(Size,:)=BestS; E=TempE; %******************************************************* end Bestfi BestS Kpidi Best_J=BestJ(G) figure(1); plot(time,BestJ,'r'); xlabel('Times');ylabel('Best J'); grid on; figure(2); plot(timef,rin,'r',timef,yout,'c'); xlabel('Time(s)');ylabel('rin,yout'); grid on