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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2005

Autores

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

Ata de Conferência Internacional

Título: IEEE Conference on Decision and Control

Páginas: 7120-7126

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

Seville, SPAIN, DEC 12-15, 2005

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 12 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7