findx0BD — Estimates state and B and D matrices of a discrete-time linear system
[X0,B,D] = findx0BD(A,C,Y,U,WITHX0,WITHD,TOL,PRINTW) [x0,B,D,V,rcnd] = findx0BD(A,C,Y,U)
state matrix of the system
C matrix of the system
system output
system input
a switch for estimating the initial state x0.
1: estimate x0;
0: do not estimate x0.
Default: WITHX0 = 1.
a switch for estimating the matrix D.
1: estimate the matrix D;
0: do not estimate the matrix D.
Default: WITHD = 1.
the tolerance used for estimating the rank of matrices. If TOL > 0, then the given value of TOL is used as a lower bound for the reciprocal condition number. Default: prod(size(matrix))*epsilon_machine where epsilon_machine is the relative machine precision.
a switch for printing the warning messages.
1: print warning messages;
0: do not print warning messages.
Default: PRINTW = 0.
intial state of the estimated linear system.
B matrix of the estimated linear system.
D matrix of the estimated linear system.
orthogonal matrix which reduces the system state matrix A to a real Schur form
estimates of the reciprocal condition numbers of the matrices involved in rank decisions.
findx0BD Estimates the initial state and/or the matrices B and D of a discrete-time linear system, given the (estimated) system matrices A, C, and a set of input/output data.
[X0,B,D] = findx0BD(A,C,Y,U,WITHX0,WITHD,TOL,PRINTW) estimates the initial state X0 and the matrices B and D of a discrete-time system using the system matrices A, C, output data Y and the input data U. The model structure is :
x(k+1) = Ax(k) + Bu(k), k >= 1, y(k) = Cx(k) + Du(k),
The vectors y(k) and u(k) are transposes of the k-th rows of Y and U, respectively.
[x0,B,D,V,rcnd] = findx0BD(A,C,Y,U) also returns the orthogonal matrix V which reduces the system state matrix A to a real Schur form, as well as some estimates of the reciprocal condition numbers of the matrices involved in rank decisions.
B = findx0BD(A,C,Y,U,0,0) returns B only, and
[B,D] = findx0BD(A,C,Y,U,0) returns B and D only.
//generate data from a given linear system
A = [ 0.5, 0.1,-0.1, 0.2;
0.1, 0, -0.1,-0.1;
-0.4,-0.6,-0.7,-0.1;
0.8, 0, -0.6,-0.6];
B = [0.8;0.1;1;-1];
C = [1 2 -1 0];
SYS=syslin(0.1,A,B,C);
nsmp=100;
U=prbs_a(nsmp,nsmp/5);
Y=(flts(U,SYS)+0.3*rand(1,nsmp,'normal'));
// Compute R
S=15;L=1;
[R,N,SVAL] = findR(S,Y',U');
N=3;
METH=3;TOL=-1;
[A,C] = findAC(S,N,L,R,METH,TOL);
[X0,B,D,V,rcnd] = findx0BD(A,C,Y',U');
SYS1=syslin(1,A,B,C,D,X0);
Y1=flts(U,SYS1);
clf();plot2d((1:nsmp)',[Y',Y1'])