IB01ND - SLICOT Library Routine Documentation

## IB01ND

### Singular value decomposition giving the system order

[Specification] [Arguments] [Method] [References] [Comments] [Example]

Purpose

      To find the singular value decomposition (SVD) giving the system
      order, using the triangular factor of the concatenated block
      Hankel matrices. Related preliminary calculations needed for
      computing the system matrices are also performed.

Specification

          SUBROUTINE IB01ND( METH, JOBD, NOBR, M, L, R, LDR, SV, TOL, IWORK,
         $                   DWORK, LDWORK, IWARN, INFO )
    C     .. Scalar Arguments ..
          DOUBLE PRECISION   TOL
          INTEGER            INFO, IWARN, L, LDR, LDWORK, M, NOBR
          CHARACTER          JOBD, METH
    C     .. Array Arguments ..
          DOUBLE PRECISION   DWORK(*), R(LDR, *), SV(*)
          INTEGER            IWORK(*)

Arguments

Mode Parameters

      METH    CHARACTER*1
              Specifies the subspace identification method to be used,
              as follows:
              = 'M':  MOESP  algorithm with past inputs and outputs;
              = 'N':  N4SID  algorithm.

      JOBD    CHARACTER*1
              Specifies whether or not the matrices B and D should later
              be computed using the MOESP approach, as follows:
              = 'M':  the matrices B and D should later be computed
                      using the MOESP approach;
              = 'N':  the matrices B and D should not be computed using
                      the MOESP approach.
              This parameter is not relevant for METH = 'N'.

Input/Output Parameters

      NOBR    (input) INTEGER
              The number of block rows,  s,  in the input and output
              block Hankel matrices.  NOBR > 0.

      M       (input) INTEGER
              The number of system inputs.  M >= 0.

      L       (input) INTEGER
              The number of system outputs.  L > 0.

      R       (input/output) DOUBLE PRECISION array, dimension
              ( LDR,2*(M+L)*NOBR )
              On entry, the leading 2*(M+L)*NOBR-by-2*(M+L)*NOBR upper
              triangular part of this array must contain the upper
              triangular factor R from the QR factorization of the
              concatenated block Hankel matrices. Denote  R_ij,
              i,j = 1:4,  the ij submatrix of  R,  partitioned by
              M*NOBR,  M*NOBR,  L*NOBR,  and  L*NOBR  rows and columns.
              On exit, if INFO = 0, the leading
              2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of this
              array contains the matrix S, the processed upper
              triangular factor R, as required by other subroutines.
              Specifically, let  S_ij, i,j = 1:4,  be the ij submatrix
              of  S,  partitioned by  M*NOBR,  L*NOBR,  M*NOBR,  and
              L*NOBR  rows and columns. The submatrix  S_22  contains
              the matrix of left singular vectors needed subsequently.
              Useful information is stored in  S_11  and in the
              block-column  S_14 : S_44.  For METH = 'M' and JOBD = 'M',
              the upper triangular part of  S_31  contains the upper
              triangular factor in the QR factorization of the matrix
              R_1c = [ R_12'  R_22'  R_11' ]',  and  S_12  contains the
              corresponding leading part of the transformed matrix
              R_2c = [ R_13'  R_23'  R_14' ]'.  For  METH = 'N',  the
              subarray  S_41 : S_43  contains the transpose of the
              matrix contained in  S_14 : S_34.

      LDR     INTEGER
              The leading dimension of the array  R.
              LDR >= MAX( 2*(M+L)*NOBR, 3*M*NOBR ),
                                   for METH = 'M' and JOBD = 'M';
              LDR >= 2*(M+L)*NOBR, for METH = 'M' and JOBD = 'N' or
                                   for METH = 'N'.

      SV      (output) DOUBLE PRECISION array, dimension ( L*NOBR )
              The singular values of the relevant part of the triangular
              factor from the QR factorization of the concatenated block
              Hankel matrices.

Tolerances

      TOL     DOUBLE PRECISION
              The tolerance to be used for estimating the rank of
              matrices. If the user sets  TOL > 0,  then the given value
              of  TOL  is used as a lower bound for the reciprocal
              condition number;  an m-by-n matrix whose estimated
              condition number is less than  1/TOL  is considered to
              be of full rank.  If the user sets  TOL <= 0,  then an
              implicitly computed, default tolerance, defined by
              TOLDEF = m*n*EPS,  is used instead, where  EPS  is the
              relative machine precision (see LAPACK Library routine
              DLAMCH).
              This parameter is not used for  METH = 'M'.

Workspace

      IWORK   INTEGER array, dimension ((M+L)*NOBR)
              This parameter is not referenced for METH = 'M'.

      DWORK   DOUBLE PRECISION array, dimension (LDWORK)
              On exit, if  INFO = 0,  DWORK(1) returns the optimal value
              of LDWORK,  and, for  METH = 'N',  DWORK(2)  and  DWORK(3)
              contain the reciprocal condition numbers of the
              triangular factors of the matrices  U_f  and  r_1  [6].
              On exit, if  INFO = -12,  DWORK(1)  returns the minimum
              value of LDWORK.

      LDWORK  INTEGER
              The length of the array DWORK.
              LDWORK >= max( (2*M-1)*NOBR, (M+L)*NOBR, 5*L*NOBR ),
                                          if METH = 'M' and JOBD = 'M';
              LDWORK >=  5*L*NOBR,        if METH = 'M' and JOBD = 'N';
              LDWORK >=  5*(M+L)*NOBR+1,  if METH = 'N'.
              For good performance,  LDWORK  should be larger.

Warning Indicator

      IWARN   INTEGER
              = 0:  no warning;
              = 4:  the least squares problems with coefficient matrix
                    U_f,  used for computing the weighted oblique
                    projection (for METH = 'N'), have a rank-deficient
                    coefficient matrix;
              = 5:  the least squares problem with coefficient matrix
                    r_1  [6], used for computing the weighted oblique
                    projection (for METH = 'N'), has a rank-deficient
                    coefficient matrix.

Error Indicator

      INFO    INTEGER
              = 0:  successful exit;
              < 0:  if INFO = -i, the i-th argument had an illegal
                    value;
              = 2:  the singular value decomposition (SVD) algorithm did
                    not converge.

Method

      A singular value decomposition (SVD) of a certain matrix is
      computed, which reveals the order  n  of the system as the number
      of "non-zero" singular values. For the MOESP approach, this matrix
      is  [ R_24'  R_34' ]' := R(ms+1:(2m+l)s,(2m+l)s+1:2(m+l)s),
      where  R  is the upper triangular factor  R  constructed by SLICOT
      Library routine  IB01MD.  For the N4SID approach, a weighted
      oblique projection is computed from the upper triangular factor  R
      and its SVD is then found.

References

      [1] Verhaegen M., and Dewilde, P.
          Subspace Model Identification. Part 1: The output-error
          state-space model identification class of algorithms.
          Int. J. Control, 56, pp. 1187-1210, 1992.

      [2] Verhaegen M.
          Subspace Model Identification. Part 3: Analysis of the
          ordinary output-error state-space model identification
          algorithm.
          Int. J. Control, 58, pp. 555-586, 1993.

      [3] Verhaegen M.
          Identification of the deterministic part of MIMO state space
          models given in innovations form from input-output data.
          Automatica, Vol.30, No.1, pp.61-74, 1994.

      [4] Van Overschee, P., and De Moor, B.
          N4SID: Subspace Algorithms for the Identification of
          Combined Deterministic-Stochastic Systems.
          Automatica, Vol.30, No.1, pp. 75-93, 1994.

      [5] Van Overschee, P., and De Moor, B.
          Subspace Identification for Linear Systems: Theory -
          Implementation - Applications.
          Kluwer Academic Publishers, Boston/London/Dordrecht, 1996.

      [6] Sima, V.
          Subspace-based Algorithms for Multivariable System
          Identification.
          Studies in Informatics and Control, 5, pp. 335-344, 1996.

Numerical Aspects

      The implemented method is numerically stable.
                                       3
      The algorithm requires 0(((m+l)s) ) floating point operations.

Further Comments

      None

Example

Program Text

      None

Program Data

      None

Program Results

      None

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