matlab.zip_PID neural network_it_neural network PID

clc clear o=input('input the order of system'); n=input('enter the coeficient of numerator'); d=input('enter the coeficient of denomenator'); c=mod(o,2); sys=tf(n,d) r=input('input the order of reduced system'); % put the last two column as "0" for i=o+1:o+2 n(1,i)=0; d(1,i+1)=0; end %for the seperate out the even & odd term %(1):for odd term if c==1 j=1; for i=1:2:o+1 ne(1,j)=n(1,i); no(1,j)=n(1,i+1); j=j+1; end j=1; for i=1:2:o+1 do(1,j)=d(1,i); de(1,j)=d(1,i+1); j=j+1; end %(2) for even term else j=1; for i=1:2:o+1 no(1,j)=n(1,i); ne(1,j)=n(1,i+1); j=j+1; end j=1; for i=1:2:o+1 de(1,j)=d(1,i); do(1,j)=d(1,i+1); j=j+1; end end n=[ne;no;de;do]; sn=tf(ne,no); sd=tf(de,do); mne=-transpose(zero(sn)); mno=-transpose(pole(sn)); mde=-transpose(zero(sd)); mdo=-transpose(pole(sd)); m=[mne; mno; mde; mdo]; for k=1:o-r % set the maximum out of the row [y,v] = max(m,[],2); % for set the end coefficint for ao,bo,co,do if c==0 n1=(o+2)/2; m1=o/2; else m1=(o-1)/2; n1=(o+1)/2; end di=zeros(4,1); for j=1:4 for i=n1:-1:1 if i==0 elseif di(j,1)==0 di(j,1)=n(j,i); end end end %find the maximum from the matrix "m" s=zeros(2,2); if y(1,1)>y(2,1) for i=1:n1-1 if y(1,1)==m(1,i) s(1,:)=[1 i]; end end else for i=1:n1-1 if y(2,1)==m(2,i) s(1,:)=[2 i]; end end end if y(3,1)>y(4,1) for i=1:n1-1 if y(3,1)==m(3,i) s(2,:)=[3 i]; end end else for i=1:n1-1 if y(4,1)==m(4,i) s(2,:)=[4 i]; end end end % instead of zero set infinity for j=1:4 for i=1:n1-1 if m(j,i)==0 i1(j,i)=-inf; else i1(j,i)=m(j,i); end end end i1(s(1,1),s(1,2))=-inf; i1(s(2,1),s(2,2))=-inf; m=i1; end % for generate the multiplication of the remaining component b=zeros(4,n1*2+1); for j=1:4 a=[0 0 di(1,1);0 di(2,1) 0 ;0 0 di(3,1); 0 di(4,1) 0 ]; b1=1; for i=1:m1+1 b1=conv(a(j,:),b1); if i==m1+1 else a(j,:)=[ (1/i1(j,i)) 0 1]; end end b(j,:)=b1; end nu1=b(1,:)+b(2,:); nu2=b(3,:)+b(4,:); rsys=tf(nu1,nu2) step(sys,rsys,'--') legend('show') figure bode(sys,rsys,'--') legend('show')