基于Matlab环境编写的一些神经网络PID控制和模糊PID控制源代码

				%BP based PID Control
				clear all;
				close all;
				
				xite=0.20;
				alfa=0.05;
				
				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);
				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);
				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];
				du_1=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;
				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));
				yout(k)=a(k)*y_1/(1+y_1^2)+u_1;
				
				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);
				
				dyu(k)=sign((yout(k)-y_1)/(du(k)-du_1+0.0001));
				
				%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
				du_1=du(k);
				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');