Purpose
To construct an upper triangular factor R of the concatenated block Hankel matrices using input-output data. The input-output data can, optionally, be processed sequentially.Specification
SUBROUTINE IB01MD( METH, ALG, 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 ALG, 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.
ALG CHARACTER*1
Specifies the algorithm for computing the triangular
factor R, as follows:
= 'C': Cholesky algorithm applied to the correlation
matrix of the input-output data;
= 'F': Fast QR algorithm;
= 'Q': QR algorithm applied to the concatenated block
Hankel matrices.
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 or input/output) DOUBLE PRECISION array, dimension
( LDR,2*(M+L)*NOBR )
On exit, if INFO = 0 and ALG = 'Q', or (ALG = 'C' or 'F',
and BATCH = 'L' or 'O'), the leading
2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of
this array contains the (current) upper triangular factor
R from the QR factorization of the concatenated block
Hankel matrices. The diagonal elements of R are positive
when the Cholesky algorithm was successfully used.
On exit, if ALG = 'C' and BATCH = 'F' or 'I', the leading
2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of this
array contains the current upper triangular part of the
correlation matrix in sequential data processing.
If ALG = 'F' and BATCH = 'F' or 'I', the array R is not
referenced.
On entry, if ALG = 'C', or ALG = 'Q', and BATCH = 'I' or
'L', the leading 2*(M+L)*NOBR-by-2*(M+L)*NOBR upper
triangular part of this array must contain the upper
triangular matrix R computed at the previous call of this
routine in sequential data processing. The array R need
not be set on entry if ALG = 'F' or if BATCH = 'F' or 'O'.
LDR INTEGER
The leading dimension of the array R.
LDR >= 2*(M+L)*NOBR.
Workspace
IWORK INTEGER array, dimension (LIWORK)
LIWORK >= M+L, if ALG = 'F';
LIWORK >= 0, if ALG = 'C' or 'Q'.
DWORK DOUBLE PRECISION array, dimension (LDWORK)
On exit, if INFO = 0, DWORK(1) returns the optimal
value of LDWORK.
On exit, if INFO = -17, DWORK(1) returns the minimum
value of LDWORK.
Let
k = 0, if CONCT = 'N' and ALG = 'C' or 'Q';
k = 2*NOBR-1, if CONCT = 'C' and ALG = 'C' or 'Q';
k = 2*NOBR*(M+L+1), if CONCT = 'N' and ALG = 'F';
k = 2*NOBR*(M+L+2), if CONCT = 'C' and ALG = 'F'.
The first (M+L)*k elements of DWORK should be preserved
during successive calls of the routine with BATCH = 'F'
or 'I', till the final call with BATCH = 'L'.
LDWORK INTEGER
The length of the array DWORK.
LDWORK >= (4*NOBR-2)*(M+L), if ALG = 'C', BATCH <> 'O' and
CONCT = 'C';
LDWORK >= 1, if ALG = 'C', BATCH = 'O' or
CONCT = 'N';
LDWORK >= (M+L)*2*NOBR*(M+L+3), if ALG = 'F',
BATCH <> 'O' and CONCT = 'C';
LDWORK >= (M+L)*2*NOBR*(M+L+1), if ALG = 'F',
BATCH = 'F', 'I' and CONCT = 'N';
LDWORK >= (M+L)*4*NOBR*(M+L+1)+(M+L)*2*NOBR, if ALG = 'F',
BATCH = 'L' and CONCT = 'N', or
BATCH = 'O';
LDWORK >= 4*(M+L)*NOBR, if ALG = 'Q', BATCH = 'F' or 'O',
and LDR >= NS = NSMP - 2*NOBR + 1;
LDWORK >= 6*(M+L)*NOBR, if ALG = 'Q', BATCH = 'F' or 'O',
and LDR < NS, or BATCH = 'I' or
'L' and CONCT = 'N';
LDWORK >= 4*(NOBR+1)*(M+L)*NOBR, if ALG = 'Q', BATCH = 'I'
or 'L' and CONCT = 'C'.
The workspace used for ALG = 'Q' is
LDRWRK*2*(M+L)*NOBR + 4*(M+L)*NOBR,
where LDRWRK = LDWORK/(2*(M+L)*NOBR) - 2; recommended
value LDRWRK = NS, assuming a large enough cache size.
For good performance, LDWORK should be larger.
If LDWORK = -1, then a workspace query is assumed;
the routine only calculates the optimal size of the
DWORK array, returns this value as the first entry of
the DWORK array, and no error message related to LDWORK
is issued by XERBLA.
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; the cycle
counter was reinitialized;
= 2: a fast algorithm was requested (ALG = 'C' or 'F'),
but it failed, and the QR algorithm was then used
(non-sequential data processing).
Error Indicator
INFO INTEGER
= 0: successful exit;
< 0: if INFO = -i, the i-th argument had an illegal
value;
= 1: a fast algorithm was requested (ALG = 'C', or 'F')
in sequential data processing, but it failed. The
routine can be repeatedly called again using the
standard QR algorithm.
Method
1) For non-sequential data processing using QR algorithm, a
t x 2(m+l)s matrix H is constructed, where
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
and Up , Uf , U , and Y are block Hankel
1,s,t s+1,2s,t 1,2s,t 1,2s,t
matrices defined in terms of the input and output data [3].
A QR factorization is used to compress the data.
The fast QR algorithm uses a QR factorization which exploits
the block-Hankel structure. Actually, the Cholesky factor of H'*H
is computed.
2) For sequential data processing using QR algorithm, the QR
decomposition is done sequentially, by updating the upper
triangular factor R. This is also performed internally if the
workspace is not large enough to accommodate an entire batch.
3) For non-sequential or sequential data processing using
Cholesky algorithm, the correlation matrix of input-output data is
computed (sequentially, if requested), taking advantage of the
block Hankel structure [7]. Then, the Cholesky factor of the
correlation matrix is found, if possible.
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] Peternell, K., Scherrer, W. and Deistler, M.
Statistical Analysis of Novel Subspace Identification Methods.
Signal Processing, 52, pp. 161-177, 1996.
[6] Sima, V.
Subspace-based Algorithms for Multivariable System
Identification.
Studies in Informatics and Control, 5, pp. 335-344, 1996.
[7] Sima, V.
Cholesky or QR Factorization for Data Compression in
Subspace-based Identification ?
Proceedings of the Second NICONET Workshop on ``Numerical
Control Software: SLICOT, a Useful Tool in Industry'',
December 3, 1999, INRIA Rocquencourt, France, pp. 75-80, 1999.
Numerical Aspects
The implemented method is numerically stable (when QR algorithm is
used), reliable and efficient. The fast Cholesky or QR algorithms
are more efficient, but the accuracy could diminish by forming the
correlation matrix.
2
The QR algorithm needs 0(t(2(m+l)s) ) floating point operations.
2 3
The Cholesky algorithm needs 0(2t(m+l) s)+0((2(m+l)s) ) floating
point operations.
2 3 2
The fast QR algorithm needs 0(2t(m+l) s)+0(4(m+l) s ) floating
point operations.
Further Comments
For ALG = 'Q', BATCH = 'O' and LDR < NS, or BATCH <> 'O', the calculations could be rather inefficient if only minimal workspace (see argument LDWORK) is provided. It is advisable to provide as much workspace as possible. Almost optimal efficiency can be obtained for LDWORK = (NS+2)*(2*(M+L)*NOBR), assuming that the cache size is large enough to accommodate R, U, Y, and DWORK.Example
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