IB01MY - SLICOT Library Routine Documentation

## IB01MY

### Upper triangular factor in the QR factorization of the block Hankel matrix, using a fast QR algorithm

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

Purpose

      To construct an upper triangular factor  R  of the concatenated
      block Hankel matrices using input-output data, via a fast QR
      algorithm based on displacement rank.  The input-output data can,
      optionally, be processed sequentially.

Specification

          SUBROUTINE IB01MY( METH, BATCH, CONCT, NOBR, M, L, NSMP, U, LDU,
         $                   Y, LDY, R, LDR, IWORK, DWORK, LDWORK, IWARN,
         $                   INFO )
    C     .. Scalar Arguments ..
          INTEGER            INFO, IWARN, L, LDR, LDU, LDWORK, LDY, M, NOBR,
         $                   NSMP
          CHARACTER          BATCH, CONCT, METH
    C     .. Array Arguments ..
          INTEGER            IWORK(*)
          DOUBLE PRECISION   DWORK(*), R(LDR, *), U(LDU, *), Y(LDY, *)

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.

      BATCH   CHARACTER*1
              Specifies whether or not sequential data processing is to
              be used, and, for sequential processing, whether or not
              the current data block is the first block, an intermediate
              block, or the last block, as follows:
              = 'F':  the first block in sequential data processing;
              = 'I':  an intermediate block in sequential data
                      processing;
              = 'L':  the last block in sequential data processing;
              = 'O':  one block only (non-sequential data processing).
              NOTE that when  100  cycles of sequential data processing
                   are completed for  BATCH = 'I',  a warning is
                   issued, to prevent for an infinite loop.

      CONCT   CHARACTER*1
              Specifies whether or not the successive data blocks in
              sequential data processing belong to a single experiment,
              as follows:
              = 'C':  the current data block is a continuation of the
                      previous data block and/or it will be continued
                      by the next data block;
              = 'N':  there is no connection between the current data
                      block and the previous and/or the next ones.
              This parameter is not used if BATCH = 'O'.

Input/Output Parameters

      NOBR    (input) INTEGER
              The number of block rows,  s,  in the input and output
              block Hankel matrices to be processed.  NOBR > 0.
              (In the MOESP theory,  NOBR  should be larger than  n, the
              estimated dimension of state vector.)

      M       (input) INTEGER
              The number of system inputs.  M >= 0.
              When M = 0, no system inputs are processed.

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

      NSMP    (input) INTEGER
              The number of rows of matrices  U  and  Y  (number of
              samples,  t). (When sequential data processing is used,
              NSMP  is the number of samples of the current data
              block.)
              NSMP >= 2*(M+L+1)*NOBR - 1,  for non-sequential
                                           processing;
              NSMP >= 2*NOBR,  for sequential processing.
              The total number of samples when calling the routine with
              BATCH = 'L'  should be at least  2*(M+L+1)*NOBR - 1.
              The  NSMP  argument may vary from a cycle to another in
              sequential data processing, but  NOBR, M,  and  L  should
              be kept constant. For efficiency, it is advisable to use
              NSMP  as large as possible.

      U       (input) DOUBLE PRECISION array, dimension (LDU,M)
              The leading NSMP-by-M part of this array must contain the
              t-by-m input-data sequence matrix  U,
              U = [u_1 u_2 ... u_m].  Column  j  of  U  contains the
              NSMP  values of the j-th input component for consecutive
              time increments.
              If M = 0, this array is not referenced.

      LDU     INTEGER
              The leading dimension of the array U.
              LDU >= NSMP, if M > 0;
              LDU >= 1,    if M = 0.

      Y       (input) DOUBLE PRECISION array, dimension (LDY,L)
              The leading NSMP-by-L part of this array must contain the
              t-by-l output-data sequence matrix  Y,
              Y = [y_1 y_2 ... y_l].  Column  j  of  Y  contains the
              NSMP  values of the j-th output component for consecutive
              time increments.

      LDY     INTEGER
              The leading dimension of the array Y.  LDY >= NSMP.

      R       (output) DOUBLE PRECISION array, dimension
              ( LDR,2*(M+L)*NOBR )
              If INFO = 0 and BATCH = 'L' or 'O', the leading
              2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of this
              array contains the upper triangular factor R from the
              QR factorization of the concatenated block Hankel
              matrices.

      LDR     INTEGER
              The leading dimension of the array  R.
              LDR >= 2*(M+L)*NOBR.

Workspace

      IWORK   INTEGER array, dimension (M+L)

      DWORK   DOUBLE PRECISION array, dimension (LDWORK)
              On exit, if  INFO = 0,  DWORK(1)  returns the optimal
              value of LDWORK.
              On exit, if  INFO = -16,  DWORK(1)  returns the minimum
              value of LDWORK.
              The first (M+L)*2*NOBR*(M+L+c) elements of  DWORK  should
              be preserved during successive calls of the routine
              with  BATCH = 'F'  or  'I',  till the final call with
              BATCH = 'L',  where
              c = 1,  if the successive data blocks do not belong to a
                      single experiment  (CONCT = 'N');
              c = 2,  if the successive data blocks belong to a single
                      experiment  (CONCT = 'C').

      LDWORK  INTEGER
              The length of the array DWORK.
              LDWORK >= (M+L)*2*NOBR*(M+L+3),
                               if BATCH <> 'O' and CONCT = 'C';
              LDWORK >= (M+L)*2*NOBR*(M+L+1),
                               if BATCH = 'F' or 'I' and CONCT = 'N';
              LDWORK >= (M+L)*4*NOBR*(M+L+1)+(M+L)*2*NOBR,
                               if BATCH = 'L' and CONCT = 'N',
                               or BATCH = 'O'.

Warning Indicator

      IWARN   INTEGER
              = 0:  no warning;
              = 1:  the number of 100 cycles in sequential data
                    processing has been exhausted without signaling
                    that the last block of data was get.

Error Indicator

      INFO    INTEGER
              = 0:  successful exit;
              < 0:  if INFO = -i, the i-th argument had an illegal
                    value;
              = 1:  the fast QR factorization algorithm failed. The
                    matrix H'*H is not (numerically) positive definite.

Method

      Consider the  t x 2(m+l)s  matrix H of concatenated block Hankel
      matrices

           H = [ Uf'         Up'      Y'      ],  for METH = 'M',
                   s+1,2s,t    1,s,t   1,2s,t

           H = [ U'       Y'      ],              for METH = 'N',
                  1,2s,t   1,2s,t

      where  Up     , Uf        , U      , and  Y        are block
               1,s,t    s+1,2s,t   1,2s,t        1,2s,t
      Hankel matrices defined in terms of the input and output data [3].
      The fast QR algorithm uses a factorization of H'*H which exploits
      the block-Hankel structure, via a displacement rank technique [5].

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] Kressner, D., Mastronardi, N., Sima, V., Van Dooren, P., and
          Van Huffel, S.
          A Fast Algorithm for Subspace State-space System
          Identification via Exploitation of the Displacement Structure.
          J. Comput. Appl. Math., Vol.132, No.1, pp. 71-81, 2001.

Numerical Aspects

      The implemented method is reliable and efficient. Numerical
      difficulties are possible when the matrix H'*H is nearly rank
      defficient. The method cannot be used if the matrix H'*H is not
      numerically positive definite.
                                      2           3 2
      The algorithm requires 0(2t(m+l) s)+0(4(m+l) s ) floating point
      operations.

Further Comments

      None

Example

Program Text

      None

Program Data

      None

Program Results

      None

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