bp神经网络用于PID控制器的参数优化,程序可以直接运行,具有很好的优化效果!

				基于bp神经网络pid控制程序Matlab
				%from pfyh
				
				%BP based PID Control
				clear all;
				close all;
				
				xite=0.25;
				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];
				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);
				if u(k)>=10 % Restricting the output of controller
				u(k)=10;
				end
				if u(k)				u(k)=-10;
				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)^);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^);