Scilab Function findAC - discrete-time system subspace identification
Calling Sequence
- [A,C] = findAC(S,N,L,R,METH,TOL,PRINTW)
- [A,C,RCND] = findAC(S,N,L,R,METH,TOL,PRINTW)
Parameters
- S
: integer, the number of block rows in the block-Hankel matrices
- N
: integer
- L
: integer
- R
: matrix, relevant part of the R factor of the concatenated block-Hankel matrices computed by a call to findr.
- METH
: integer, an option for the method to use
- = 1
: MOESP method with past inputs and outputs;
- = 2
: N4SID method;
Default: METH = 3.
- TOL
: 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.
- PRINTW
: integer, switch for printing the warning messages.
- PRINTW
= 1: print warning messages;
- = 0: do not print warning messages.
Default: PRINTW = 0.
- A
: matrix, state system matrix
- C
: matrix, output system matrix
- RCND
: vector of length 4, condition numbers of the matrices involved in rank decision
Description
finds the system matrices A and C of a discrete-time system, given the
system order and the relevant part of the R factor of the concatenated
block-Hankel matrices, using subspace identification techniques (MOESP
or N4SID).
*
[A,C] = findAC(S,N,L,R,METH,TOL,PRINTW) computes the system matrices A and C. The model structure is: x(k+1) = Ax(k) + Bu(k) + Ke(k), k >= 1, y(k) = Cx(k) + Du(k) + e(k), where x(k) and y(k) are vectors of length N and L, respectively.
*
[A,C,RCND] = findAC(S,N,L,R,METH,TOL,PRINTW) also returns the vector RCND of length 4 containing the condition numbers of the matrices involved in rank decisions.
Matrix R, computed by findR, should be determined with suitable arguments
METH and JOBD.
Examples
//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);
See Also