MATLAB平台下的基于人工神经网络的非线性控制系统PID参数整定代码

				%基于bp神经网络pid控制程序
				
				
				%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');