Extended Ho-Kalman algorithm for systems represented in generalized orthonormal bases

Authors:
ZoltáN Szabó;Peter S. C. Heuberger;JóZsef Bokor;Paul M. J. Van Den Hof
Affiliations:
Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, H 1502 Budapest, Hungary;Department of Applied Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, Netherlands;Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, H 1502 Budapest, Hungary;Department of Applied Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, Netherlands
Venue:
Automatica (Journal of IFAC)
Year:
2000

Citing 3
Cited 1

System identification with generalized orthonormal basis functions

Automatica (Journal of IFAC) - Special issue on trends in system identification
On approximation of stable linear dynamical systems using Laguerre and Kautz functions

Automatica (Journal of IFAC)
Approximate identification in Laguerre and Kautz bases

Automatica (Journal of IFAC)

Minimal partial realization from generalized orthonormal basis function expansions

Automatica (Journal of IFAC)

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Abstract

This paper considers the construction of minimal state space models of linear time-invariant systems on the basis of system representations in terms of generalized orthogonal basis function expansions. Starting from the classical Ho-Kalman algorithm that solves the problem using Markov parameter expansions, a generalization is obtained by analysing the matrix representations of the Hankel operators in generalized orthonormal bases. Using the so-called Hambo-domain techniques an efficient algorithm is given to implement the proposed method.