基于Matlab实现梯度下降法的RBF神经网络控制（源码）.rar

%RBF Supervisory Control clear all; close all; ts=0.001; sys=tf(1000,[1,50,2000]); dsys=c2d(sys,ts,'z'); [num,den]=tfdata(dsys,'v'); y_1=0;y_2=0; u_1=0;u_2=0; e_1=0; xi=0; x=[0,0]'; b=0.5*ones(4,1); c=[-2 -1 1 2]; w=rands(4,1); w_1=w; w_2=w_1; xite=0.30; alfa=0.05; kp=25; kd=0.3; for k=1:1:1000 time(k)=k*ts; S=1; if S==1 yd(k)=0.5*sign(sin(2*2*pi*k*ts)); %Square Signal elseif S==2 yd(k)=0.5*(sin(3*2*pi*k*ts)); %Square Signal end y(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2; e(k)=yd(k)-y(k); xi=yd(k); for j=1:1:4 h(j)=exp(-norm(xi-c(:,j))^2/(2*b(j)*b(j))); end un(k)=w'*h'; %PD Controller up(k)=kp*x(1)+kd*x(2); M=2; if M==1 %Only Using PID Control u(k)=up(k); elseif M==2 %Total control output u(k)=up(k)+un(k); end if u(k)>=10 u(k)=10; end if u(k)<=-10 u(k)=-10; end %Update NN Weight d_w=-xite*(un(k)-u(k))*h'; w=w_1+ d_w+alfa*(w_1-w_2); w_2=w_1; w_1=w; u_2=u_1; u_1=u(k); y_2=y_1; y_1=y(k); x(1)=e(k); %Calculating P x(2)=(e(k)-e_1)/ts; %Calculating D e_1=e(k); end figure(1); plot(time,yd,'r',time,y,'k:','linewidth',2); xlabel('time(s)');ylabel('Position tracking'); legend('Ideal position signal','Tracking position signal'); figure(2); subplot(311); plot(time,un,'k','linewidth',2); xlabel('time(s)');ylabel('un'); legend('Control input with RBF'); subplot(312); plot(time,up,'k','linewidth',2); xlabel('time(s)');ylabel('up'); legend('Control input with P'); subplot(313); plot(time,u,'k','linewidth',2); xlabel('time(s)');ylabel('u'); legend('Total control input');