CCE Faculty Books and Book Chapters
Indirect continuous-time LPV system identification through a downsampled subspace approach
Book Title
Linear parameter-varying system identification, new developments and trends (Advanced series in electrical and computer engineering)
ORCID
Jose A. Ramos0000-0003-4966-4265
Document Type
Book Chapter
ISBN
9789814355445
Publication Date
2012
Editors
Lopes Do Santos, P., Azevedo Perdicoulis, T.P., Novara, C., Ramos, J.A., Rivera, D.E.
Description
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.
DOI
https://doi.org/10.1142/9789814355452_0009
Publisher
World Scientific Publishing
City
Singapore
First Page
231
Last Page
258
Disciplines
Computer Sciences
NSUWorks Citation
Lopes dos Santos, Paulo; Azevedo Perdicoulis, Teresa Paula; Ramos, Jose A.; and Martins de Carvalho, Jorge L., "Indirect continuous-time LPV system identification through a downsampled subspace approach" (2012). CCE Faculty Books and Book Chapters. 29.
https://nsuworks.nova.edu/gscis_facbooks/29
Additional Information
pp. 231-258