Subspace identification of multivariable linear parameter-varying systems

Authors:
Vincent Verdult;Michel Verhaegen
Affiliations:
Delft University of Technology, Faculty of Information Technology and Systems, Control Systems Engineering, P.O. Box 5031, NL-2600 GA Delft, The Netherlands;Delft University of Technology, Faculty of Information Technology and Systems, Control Systems Engineering, P.O. Box 5031, NL-2600 GA Delft, The Netherlands
Venue:
Automatica (Journal of IFAC)
Year:
2002

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Abstract

A subspace identification method is discussed that deals with multivariable linear parameter-varying state-space systems with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of system is the enormous dimension of the data matrices involved. To overcome the curse of dimensionality, we suggest using only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but processes them row by row.