Multiple Model Adaptive Estimation and Model Identification using a Minimum Energy Criterion

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Vahid Hassani (Autor) Outra; Pedro P Aguiar (Autor) Outra; Michael Athans (Autor) Outra; Antonio M Pascoal (Autor) Outra

Ata de Conferência Internacional

Título: 2009 AMERICAN CONTROL CONFERENCE, VOLS 1-9

Páginas: 518-523

American Control Conference 2009

St Louis, MO, JUN 10-12, 2009

Indexação

ISI Web of Knowledge - 16 Citações

Scopus - 30 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-003-SNM

DOI: 10.1109/acc.2009.5160446

Abstract (EN): This paper addresses the problem of Multiple Model Adaptive Estimation (MMAE) for discrete-time, linear, time-invariant MIMO plants with parameter uncertainty and unmodeled dynamics. Model identification is analyzed in a deterministic setting by adopting a Minimum Energy selection criterion. The MMAE system relies on a finite number of local observers, each designed using a selected model (SM) from the original set of possibly infinite plant models. Results akin to those previously obtained in a stochastic setting are derived in a far simpler manner, in a deterministic framework. We show, under suitable distinguishability conditions, that the SM identified is the one that corresponds to the observer with smallest output prediction error energy. We also develop a procedure to analyze the behavior of MMAE when the true plant is not one of the SMs. This leads to an algorithm that computes, for each SM, the set of equivalently identified plants, that is, the set of plants that will be identified as that particular SM. The impact of unmodeled dynamics on model identification is discussed. Simulation results with a model of a motor coupled to a load via an elastic shaft illustrate the performance of the methodology derived.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6