
模糊PID参数自整定(共9页).doc
9页精选优质文档-----倾情为你奉上模糊PID参数自整定设被控对象为采样时间为1ms,采样模糊PID控制进行阶跃响应,在第300个采样时间时控制加1.0的干扰,相应的响应结果如下图:Ki的录属度函数Kp的录属度函数e的录属度函数ec的录属度函数下面是系统在外界有干扰输入时普通PID和模糊PID参数自整定控制的阶跃响应曲线:模糊PID控制阶跃响应普通PID控制阶跃响应. 从上面的仿真图可以看出,经过模糊PID参数自整定后,在外界干扰输入时,可以得到很好的控制效果下面是PID参数的整定曲线:. Kp的自整定调整 Ki的自整定调整Kd的自整定调整程序文本:%Fuzzy Tunning PID Controlclear all;close 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 kpa=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 P x(2)=error(k)-error_1; % Calculating D x(3)=x(3)+error(k); % Calculating I e_1=x(1); ec_1=x(2); error_2=error_1; error_1=error(k);。












