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Please note that the recommended version of Scilab is 2026.0.0. This page might be outdated.
See the recommended documentation of this 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)
Arguments
- 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);
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