Identification of linear parameter varying systems using an iterative deterministic-stochastic subspace approach

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

P. Lopes dos Santos (Author) FEUP; Ramos, JA (Author) Other; Martins De Carvalho, JL (Author) Other

Conference proceedings International

Title: 2007 European Control Conference, ECC 2007

Pages: 4867-4873

2007 9th European Control Conference, ECC 2007

2 July 2007 through 5 July 2007

Indexing

Scopus - 31 Citations

Other information

Authenticus ID: P-00A-C0C

Abstract (EN): In this paper we introduce a recursive subspace system identification algorithm for MIMO linear parameter varying systems driven by general inputs and a white noise time varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. The key to the proposed algorithm is the fact that the bilinear term between the time varying parameter vector and the state vector behaves like a white noise process. Using a linear Kalman filter model, the bilinear term can be efficiently estimated and then used to construct an augmented input vector at each iteration. Since the previous state is known at each iteration, the system becomes linear, which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a linear parameter varying model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality. © 2007 EUCA.

Language: English

Type (Professor's evaluation): Scientific