Indirect continuous-time LPV system identification through a downsampled subspace approach

• Type: Chapter or Part of a Book
• Date: 2011

Authors

P. Lopes dos Santos (Author) FEUP; Perdicoúlis, TPA (Author) Other; Ramos, JA (Author) Other; Carvalho, JLM (Author) Other

Book

Title: Linear Parameter-varying System Identification: New Developments And Trends

Pages: 231-258

ISBN: 9789814355452

Publisher: World Scientific Publishing Co.

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00H-YA5

DOI: 10.1142/9789814355452_0009

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. © 2012 by World Scientific Publishing Co. Pte. Ltd.

Language: English

Type (Professor's evaluation): Scientific