bp_pid1.rar_bp pid_neural network pid_neural network PID

%BP based PID Control clear all; close all; xite=0.25; %惯性系数 alfa=0.05; %学习率 S=2; %Signal type IN=4;H=5;Out=3; %NN Structure if S==1 %Step Signal wi=[-0.6394 -0.2696 -0.3756 -0.7023; -0.8603 -0.2013 -0.5024 -0.2596; -1.0749 0.5543 -1.6820 -0.5437; -0.3625 -0.0724 -0.6463 -0.2859; 0.1425 0.0279 -0.5406 -0.7660]; %wi=0.50*rands(H,IN); wi_1=wi;wi_2=wi;wi_3=wi; wo=[0.7576 0.2616 0.5820 -0.1416 -0.1325; -0.1146 0.2949 0.8352 0.2205 0.4508; 0.7201 0.4566 0.7672 0.4962 0.3632]; %wo=0.50*rands(Out,H); %初始化输入层 wo_1=wo;wo_2=wo;wo_3=wo; end if S==2 %Sine Signal wi=[-0.2846 0.2193 -0.5097 -1.0668; -0.7484 -0.1210 -0.4708 0.0988; -0.7176 0.8297 -1.6000 0.2049; -0.0858 0.1925 -0.6346 0.0347; 0.4358 0.2369 -0.4564 -0.1324]; %wi=0.50*rands(H,IN); %初始化输出层 wi_1=wi;wi_2=wi;wi_3=wi; wo=[1.0438 0.5478 0.8682 0.1446 0.1537; 0.1716 0.5811 1.1214 0.5067 0.7370; 1.0063 0.7428 1.0534 0.7824 0.6494]; %wo=0.50*rands(Out,H); wo_1=wo;wo_2=wo;wo_3=wo; end 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=0.001; for k=1:1:6000 time(k)=k*ts; if S==1 rin(k)=1.0; %参考输入 elseif S==2 rin(k)=sin(1*2*pi*k*ts); end %Unlinear model a(k)=1.2*(1-0.8*exp(-0.1*k)); %系数 yout(k)=a(k)*y_1/(1+y_1^2)+u_1; %对象输出差分方程 error(k)=rin(k)-yout(k); %误差 xi=[rin(k),yout(k),error(k),1]; %神经网络输入 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'; %隐含层输入 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 输出层输入 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); %输出层输出PID三个系数 Kpid=[kp(k),ki(k),kd(k)]; du(k)=Kpid*epid; u(k)=u_1+du(k); if u(k)>=10 % Restricting the output of controller u(k)=10; end if u(k)<=-10 u(k)=-10; end dyu(k)=sign((yout(k)-y_1)/(u(k)-u_1+0.0000001)); %输出层权值整定 %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)');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(312); plot(time,ki,'g'); xlabel('time(s)');ylabel('ki'); subplot(313); plot(time,kd,'b'); xlabel('time(s)');ylabel('kd');