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LPV Indirect Continuous-Time System Identification Through a Downsampled Subspace Approach
Title
LPV Indirect Continuous-Time System Identification Through a Downsampled Subspace Approach
Type
Chapter or Part of a Book
Year
2011
Authors
P. Lopes dos Santos
(Author)
FEUP
T-P Azevedo Perdicoúlis
(Author)
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José A. Ramos
(Author)
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Jorge Leite Martins de Carvalho
(Author)
FEUP
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Book
Title:
Linear Parameter-varying System Identification: New Developments And TrendsImported from Authenticus
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Pages: 231-258
ISBN:
9789814355452
Scientific classification
FOS:
Engineering and technology > Electrical engineering, Electronic engineering, Information engineering
CORDIS:
Technological sciences > Engineering > Control engineering
Other information
Abstract (EN):
The successive approximation Linear Parameter Varying systems subspace identification algorithm for discrete-time systems is based on a convergent
sequence of linear time invariant deterministic stochastic state-space approximations. In this chapter, this method is modified to cope
with continuous-time LPV state-space models. To do this, the LPV system is discretised, the discrete-time model is identified by the successive
approximations algorithm and then converted to a continuous-time model. Since affine dependence is preserved only for fast sampling, a
subspace downsampling approach is used to estimate the linear time invariant deterministic-stochastic state-space approximations. A second order simulation example, with complex poles, illustrates the effectiveness of the new algorithm.
Language:
English
Type (Professor's evaluation):
Scientific
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