BP神经网络整定的PID算法_matlab源程序.docVIP

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 Control clear all; close all; xite=0.28; alfa=0.001; IN=4;H=5;Out=3; %NN Structure wi=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 layer I=Oh; %Input to NN middle layer error_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=1:1:2000 time(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 Layer end K=wo*Oh; %Output Layer for l=1:1:Out K(l)=exp(K(l))/(exp(K(l))+exp(-K(l))); %Getting kp,ki,kd end kp(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; end if u(k)=-45 u(k)=-45; end dyu(k)=sign((yout(k)-y_1)/(u(k)-u_1+0.0000001)); %Output layer for j=1:1:Out dK(j)=2/(exp(K(j))+exp(-K(j)))^2; end for l=1:1:Out delta3(l)=error(k)*dyu(k)*epid(l)*dK(l); end for l=1:1:Out for i=1:1:H d_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2); end end wo=wo_1+d_wo+alfa*(wo_1-wo_2); %Hidden layer for 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); end d_wi=xite*delta2*xi; wi=wi_1+d_wi+alfa*(wi_1-wi_2); %Parameters Update u_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); end figure(1); plot(time,rin,r,time,yout,b); xlabel(time(s));ylabel(rin,yout); figure(2); plot(time,error,r); xlabel(time(s));ylabe