模糊自适应整定PID控制matlab仿真程序(刘金锟-先进PID控制及其MATLAB仿真)

这个例子的程序百度文库里有很多版本，但我下了很多都有错误，运行不了。以下程序我一字一字的敲出来的，已经成功运行，绝对无误。仿真实例，被控对象为(s)=采样时间为1ms，采用模糊PID控制进行阶跃响应，在第300个采样时间时控制器输出加1.0的干扰，相应的运行结果如图113所示。仿真程序如下：将以下程序保存为fuzzypid.m文件，即可得到仿真结果。%fuzzy tunning PID controlclear all;clear all; a=newfis(fuzzpid); a=addvar(a,input,e,-3,3); %parameter ea=addmf(a,input,1,NB,zmf,-3,-1);a=addmf(a,input,1,NM,trimf,-3,-2,0);a=addmf(a,input,1,NS,trimf,-3,-1,1);a=addmf(a,input,1,Z,trimf,-2,0,2);a=addmf(a,input,1,PS,trimf,-1,1,3);a=addmf(a,input,1,PM,trimf,0,2,3);a=addmf(a,input,1,PB,smf,1,3); a=addvar(a,input,ec,-3,3); %parameter eca=addmf(a,input,2,NB,zmf,-3,-1);a=addmf(a,input,2,NM,trimf,-3,-2,0);a=addmf(a,input,2,NS,trimf,-3,-1,1);a=addmf(a,input,2,Z,trimf,-2,0,2);a=addmf(a,input,2,PS,trimf,-1,1,3);a=addmf(a,input,2,PM,trimf,0,2,3);a=addmf(a,input,2,PB,smf,1,3); a=addvar(a,output,kp,-0.3,0.3); %parameter kpa=addmf(a,output,1,NB,zmf,-0.3,-0.1);a=addmf(a,output,1,NM,trimf,-0.3,-0.2,0);a=addmf(a,output,1,NS,trimf,-0.3,-0.1,0.1);a=addmf(a,output,1,Z,trimf,-0.2,0,0.2);a=addmf(a,output,1,PS,trimf,-0.1,0.1,0.3);a=addmf(a,output,1,PM,trimf,0,0.2,0.3);a=addmf(a,output,1,PB,smf,0.1,0.3); a=addvar(a,output,ki,-0.06,0.06); %parameter kia=addmf(a,output,2,NB,zmf,-0.06,-0.02);a=addmf(a,output,2,NM,trimf,-0.06,-0.04,0);a=addmf(a,output,2,NS,trimf,-0.06,-0.02,0.02);a=addmf(a,output,2,Z,trimf,-0.04,0,0.04);a=addmf(a,output,2,PS,trimf,-0.02,0.02,0.06);a=addmf(a,output,2,PM,trimf,0,0.04,0.06);a=addmf(a,output,2,PB,smf,0.02,0.06); a=addvar(a,output,kd,-3,3); %parameter kda=addmf(a,output,3,NB,zmf,-3,-1);a=addmf(a,output,3,NM,trimf,-3,-2,0);a=addmf(a,output,3,NS,trimf,-3,-1,1);a=addmf(a,output,3,Z,trimf,-2,0,2);a=addmf(a,output,3,PS,trimf,-1,1,3);a=addmf(a,output,3,PM,trimf,0,2,3);a=addmf(a,output,3,PB,smf,1,3); rulelist=1 1 7 1 5 1 1; 1 2 7 1 3 1 1; 1 3 6 2 1 1 1; 1 4 6 2 1 1 1; 1 5 5 3 1 1 1; 1 6 4 4 2 1 1; 1 7 4 4 5 1 1; 2 1 7 1 5 1 1; 2 2 7 1 3 1 1; 2 3 6 2 1 1 1; 2 4 5 3 2 1 1; 2 5 5 3 2 1 1; 2 6 4 4 3 1 1; 2 7 3 4 4 1 1; 3 1 6 1 4 1 1; 3 2 6 2 3 1 1; 3 3 6 3 2 1 1; 3 4 5 3 2 1 1; 3 5 4 4 3 1 1; 3 6 3 5 3 1 1; 3 7 3 5 4 1 1; 4 1 6 2 4 1 1; 4 2 6 2 3 1 1; 4 3 5 3 3 1 1; 4 4 4 4 3 1 1; 4 5 3 5 3 1 1; 4 6 2 6 3 1 1; 4 7 2 6 4 1 1; 5 1 5 2 4 1 1; 5 2 5 3 4 1 1; 5 3 4 4 4 1 1; 5 4 3 5 4 1 1; 5 5 3 5 4 1 1; 5 6 2 6 4 1 1; 5 7 2 7 4 1 1; 6 1 5 4 7 1 1; 6 2 4 4 5 1 1; 6 3 3 5 5 1 1; 6 4 2 5 5 1 1; 6 5 2 6 5 1 1; 6 6 2 7 5 1 1; 6 7 1 7 7 1 1; 7 1 4 4 7 1 1; 7 2 4 4 6 1 1; 7 3 2 5 6 1 1; 7 4 2 6 6 1 1; 7 5 2 6 5 1 1; 7 6 1 7 5 1 1; 7 7 1 7 7 1 1; a=addrule(a,rulelist);a=setfis(a,DefuzzMethod,mom);writefis(a,fuzzpid); a=readfis(fuzzpid); %PID controllerts=0.001;sys=tf(5.235e005,1,87.35,1.047e004,0);dsys=c2d(sys,ts,tustin);num,den=tfdata(dsys,v); u_1=0.0;u_2=0.0;u_3=0.0; y_1=0;y_2=0;y_3=0; x=0,0,0; error_1=0;e_1=0.0;ec_1=0.0; kp0=0.40;kd0=1.0;ki0=0.0; for k=1:1:500time(k)=k*ts; rin(k)=1; %using fuzzy inference to tunning PIDk_pid=evalfis(e_1,ec_1,a);kp(k)=kp0+k_pid(1);ki(k)=ki0+k_pid(2);kd(k)=kd0+k_pid(3); u(k)=kp(k)*x(1)+kd(k)*x(2)+ki(k)*x(3); if k=300 %adding disturbance(1.0v at time 0.3s) u(k)=u(k)+1.0;endif u(k)=10 u(k)=10;endif u(k)=-10 u(k)=-10;end yout(k)=-den(2)*y_1-den(3)*y_2-den(4)*y_3+num(1)*u(k)+num(2)*u_1+num(3)*u_2+num(4)*u_3;error(k)=rin(k)-yout(k);%return of pid parameters%u_3=u_2;u_2=u_1;u_1=u(k); y_3=y_2;y_2=y_1;y_1=yout(k); x(1)=error(k); %calculating Px(2)=error(k)-error_1; %calculating Dx(3)=x(3)+error(k); %calculating D e_1=x(1);ec_1=x(2); error_2=error_1;error_1=error(k); endshowrule(a) figure(1);plot(time,rin,b,time,yout,r);xlabel(time(s);ylabel( rin,yout); figure(2);plot(time,error,r);xlabel(time(s);ylabel( error ); figure(3);plot(time,u,r);xlabel(time(s);ylabel( u ); figure(4);plot(time,kp,r);xlabel(time(s);ylabel( kp ); figure(5);plot(time,ki,r);xlabel(time(s);ylabel( ki ); figure(6);plot(time,kd,r);xlabel(time(