BP神经网络整定的PID算法-matlab源程序.docx

BP神经网络整定的PID控制算法matlab源程序，系统为二阶闭环系统。%BP based PID Controlclear all;close all;xite=0.28;alfa=0.001;IN=4;H=5;Out=3; %NN Structurewi=0.50*rands(H,IN);wi_1=wi;wi_2=wi;wi_3=wi;wo=0.50*rands(Out,H);wo_1=wo;wo_2=wo;wo_3=wo;x=0,0,0;u_1=0;u_2=0;u_3=0;u_4=0;u_5=0;y_1=0;y_2=0;y_3=0;Oh=zeros(H,1); %Output from NN middle layerI=Oh; %Input to NN middle layererror_2=0;error_1=0;ts=0.01;sys=tf(2.6126,1,3.201,2.7225); %建立被控对象传递函数dsys=c2d(sys,ts,z); %把传递函数离散化num,den=tfdata(dsys,v); %离散化后提取分子、分母for k=1:1:2000time(k)=k*ts;rin(k)=40;yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_2+num(3)*u_3;error(k)=rin(k)-yout(k);xi=rin(k),yout(k),error(k),1;x(1)=error(k)-error_1;x(2)=error(k);x(3)=error(k)-2*error_1+error_2;epid=x(1);x(2);x(3);I=xi*wi;for j=1:1:H Oh(j)=(exp(I(j)-exp(-I(j)/(exp(I(j)+exp(-I(j); %Middle LayerendK=wo*Oh; %Output Layerfor l=1:1:Out K(l)=exp(K(l)/(exp(K(l)+exp(-K(l); %Getting kp,ki,kdendkp(k)=K(1);ki(k)=K(2);kd(k)=K(3);Kpid=kp(k),ki(k),kd(k);du(k)=Kpid*epid;u(k)=u_1+du(k);if u(k)=45 % Restricting the output of controller u(k)=45;endif u(k)=-45 u(k)=-45;enddyu(k)=sign(yout(k)-y_1)/(u(k)-u_1+0.0000001);%Output layerfor j=1:1:Out dK(j)=2/(exp(K(j)+exp(-K(j)2;endfor l=1:1:Out delta3(l)=error(k)*dyu(k)*epid(l)*dK(l);endfor l=1:1:Out for i=1:1:H d_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2); endend wo=wo_1+d_wo+alfa*(wo_1-wo_2);%Hidden layerfor i=1:1:H dO(i)=4/(exp(I(i)+exp(-I(i)2;end segma=delta3*wo;for i=1:1:H delta2(i)=dO(i)*segma(i);endd_wi=xite*delta2*xi;wi=wi_1+d_wi+alfa*(wi_1-wi_2);%Parameters Updateu_5=u_4;u_4=u_3;u_3=u_2;u_2=u_1;u_1=u(k); y_2=y_1;y_1=yout(k); wo_3=wo_2;wo_2=wo_1;wo_1=wo; wi_3=wi_2;wi_2=wi_1;wi_1=wi;error_2=error_1;error_1=error(k);endfigure(1);plot(time,rin,r,time,yout,b);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);subplot(311);plot(time,kp,r);xlabel(time(s);ylabel(kp);subplot(31