Scilab Function

findAC - discrete-time system subspace identification

### Sequence d'appel

  `[A,C] = findAC(S,N,L,R,METH,TOL,PRINTW)`
  `[A,C,RCND] = findAC(S,N,L,R,METH,TOL,PRINTW)`

### Parametres

- `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.

### Exemples

    //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);

### Voir aussi

`findABCD` `findBD` `findBDK` `findR` `sorder` `sident`