matlab神经网络PID控制.rar_constructiond82_控制_控制优化_网络控制

%Single Neural Net PID Controller based on RBF Identification clear all;close all; Jp=0.0030;bp=0.067; ts=0.001; Gp=tf([1],[Jp,bp,0]); Gpz=c2d(Gp,ts,'z'); [num,den]=tfdata(Gpz,'v'); h=zeros(6,1); %w=rands(6,1); w=[-0.5646; 0.3937; -0.5556; 0.3981; 0.4495; 0.2565]; w_1=w;w_2=w;w_3=w; xite=0.40; alfa=0.05; belte=0.01; x=[0,0,0]'; %c=0.1*ones(3,6); %b=0.1*ones(6,1); c=[-3.1829 -0.5211 7.1754 11.6631 -3.6992 -10.9150; -3.8909 2.3999 5.1730 8.5871 -11.3737 -7.0179; -4.2018 2.6742 5.1828 8.5238 -1.8936 -6.1845]; b=[ 5.3074; 1.4771; 26.4114; 22.1716; 52.9082; 5.6906]; c_1=c;c_2=c_1;c_3=c_2; b_1=b;b_2=b_1;b_3=b_2; xc=[0,0,0]'; xitec=0.60; kp=80; kd=5; ki=50; wc=[kp,kd,ki]; wc_1=wc;wc_2=wc;wc_3=wc; error_1=0;error_2=0; y_1=0;y_2=0; u_1=0;u_2=0; ei=0; c_size=size(c); for k=1:1:1000 time(k)=k*ts; rin(k)=0.50*sin(3*2*pi*k*ts); S=2; if S==1 yrout(k)=1.0*rin(k); end if S==2 yrout(k)=0.2*y_1+0.6*rin(k); %Reference Model end %Linear model yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2; for j=1:1:c_size(2), h(j)=exp(-norm(x-c_1(:,j))^2/(2*b_1(j)*b_1(j))); end ymout(k)=w_1'*h; id=abs(yout(k)-ymout(k)); if id>0.0001, %-----------------Adjusting RBF parameters-----------------------% d_w=0*w; % Defining matrix number of d_w equal to that of w for j=1:1:6 d_w(j)=xite*(yout(k)-ymout(k))*h(j); end w=w_1+d_w+alfa*(w_1-w_2)+belte*(w_2-w_3); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% d_b=0*b; for j=1:1:6 d_b(j)=xite*(yout(k)-ymout(k))*w_1(j)*h(j)*(b_1(j)^-3)*norm(x-c_1(:,j))^2; end b=b_1+ d_b+alfa*(b_1-b_2)+belte*(b_2-b_3); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for j=1:1:6 for i=1:1:3 d_c(i,j)=xite*(yout(k)-ymout(k))*w_1(j)*h(j)*(x(i)-c_1(i,j))*(b_1(j)^-2); end end c=c_1+d_c+alfa*(c_1-c_2)+belte*(c_2-c_3); end %%%%%%%%%%% Calculating Jacobian %%%%%%%%%%%% dyu=0; for j=1:1:c_size(2) dyu=dyu+w(j)*h(j)*(-x(1)+c(1,j))/b(j)^2; end dyout(k)=dyu; %%%%%%Parameters Return%%%%%%% error(k)=yrout(k)-yout(k); xc(1)=error(k); xc(2)=(error(k)-error_1)/ts; ei=ei+error(k)*ts; xc(3)=ei; u(k)=wc*xc; %Control law if u(k)>10, u(k)=10; end if u(k)<-10, u(k)=-10; end d_wc=0*wc; % Defining matrix number of d_w equal to that of w for j=1:1:3 d_wc(j)=xitec*error(k)*xc(j)*dyout(k); end wc=wc_1+d_wc+alfa*(wc_1-wc_2)+belte*(wc_2-wc_3); error_2=error_1;error_1=error(k); u_2=u_1;u_1=u(k); y_2=y_1;y_1=yout(k); x(3)=y_2; x(2)=y_1; x(1)=u_1; w_3=w_2;w_2=w_1;w_1=w; c_3=c_2;c_2=c_1;c_1=c; b_3=b_2;b_2=b_1;b_1=b; wc_3=wc_2;wc_2=wc_1;wc_1=wc; end figure(1); plot(time,yout,'r',time,ymout,'b'); xlabel('time(s)');ylabel('yout,ymout'); figure(2); plot(time,dyout,'r'); xlabel('time(s)');ylabel('Jacobian value'); figure(3); plot(time,yrout,'b',time,yout,'r'); xlabel('time(s)');ylabel('yrout,yout'); figure(4); plot(time,yrout-yout,'r'); xlabel('time(s)');ylabel('control error');