Identification of bilinear systems using an iterative deterministic-stochastic subspace approach

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

P. Lopes dos Santos (Author) FEUP; Ramos, MJ (Author) FCUP; de Carvalho, JLM (Author) Other

Conference proceedings International

Title: IEEE Conference on Decision and Control

Pages: 7120-7126

44th IEEE Conference on Decision Control/European Control Conference (CCD-ECC)

Seville, SPAIN, DEC 12-15, 2005

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-000-6ZM

DOI: 10.1109/cdc.2005.1583309

Abstract (EN): In this paper we introduce a new identification algorithm for MIMO bilinear systems driven by white noise inputs. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state space approximations, thus considered a Picard based method. The key to the algorithm is the fact that the bilinear terms behave like white noise processes. Using a linear Kalman filter, the bilinear terms can be estimated and combined with the system inputs at each iteration, leading to a linear system 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 bilinear model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7