JGPC.rar_JGPC_广义预测控制_隐式 广义预测_隐式预测控制

clear nn=input('Length of time domain nn='); n=input('Length of prediction n='); m=input('Length of control domain m='); t0=input('control weighting efficient='); a=input('Diffusion coefficient a='); t1=1; d1=input('format of (n+1) order square P:0- duijiao,1-square'); d2=input('original value of (n+1) order square P:'); if(d1==1) if(d2==1) P=input('Input the value of square P:'); else P=(1e+5)*eye(n+1); end else if (d2==1) PP=input('Input the value of diagonal matrix P;'); P=diag(PP); else P=(1e+5)*ones(n+1); end end % original value uuu=0;yyy=0; uu=zeros(n,1);u=zeros(m,1); yy=zeros(n,1);y1=zeros(n,1); Q=zeros(n,1);Q(1,1)=1;Q(n+1,1)=1; % square signal T=300;[yr0,t]=gensig('square',100,T,1); nm=length(t); for ij=2:nm yr=yr0(ij)+1; y=2.5*yy(n,1)-2.2*yy(n-1,1)+0.7*yy(n-2,1)+(uu(n,1)+1.5*uu(n-1,1))/12.5; a9=0; for i=1:1 a9=a9+rand; end a8=0.01*(a9-6); for i=1:n-1 yy(i,1)=yy(i+1,1); end yy(n,1)=y; yyy=[yyy;y]; for i=1:n X(1,i)=uu(i,1); end X(1,n+1)=1; K=P*X'*inv(t1+X*P*X'); P=(eye(n+1)-K*X)*P/t1; Q=Q+K*(y-X*Q); for j=1:m for i=n:-1:j i1=n-i+j; G(i1,j)=Q(i,1); end end % 根据y1，求n维f向量 e=y-y1(1,1); f(1:n-1,1)=y1(2:n,1)+e; f(n,1)=y1(n,1)+e; y1=f+G*u; % 由当前k时刻输出值y(k)和给定值yr，求k时刻以后的n个参考轨迹 w=a*y+(1-a)*yr; for i=2:n w=[w;a^i*y+(1-a^i)*yr]; end u=inv(G'*G+t0*eye(m))*G'*(w-f); for i=1:n-1 uu(i,1)=uu(i+1,1); end uu(n,1)=u(1,1); uuu=[uuu;u(1,1)]; if (u(1,1)>0.5*yr) u(1,1)=0.5*yr; end if (u(1,1)<-0.5*yr) u(1,1)=-0.5*yr; end end subplot(2,1,1);plot(t,(yr0+1),t,yyy); axis([0,nm,0,2.5]);xlabel('t');ylabel('yr,y') subplot(2,1,2);plot(t,uuu); axis([0,nm,-1.5,1.5]);xlabel('t');ylabel('u')