Robust Kalman Filtering for Signals and Systems with Large Uncertainties

Andrey V. Savkin

ISBN 10: 1461272092 / ISBN 13: 9781461272090

Editore: Birkhäuser Boston, Birkhäuser Boston Nov 2012, 2012

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This item is printed on demand - Print on Demand Titel. Neuware -A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in terested in problems of robust filtering through his research collaboration with Dr. Duncan McFarlane. At this time, Dr. McFarlane was employed at the Melbourne Research Laboratories ofBHP Ltd. , a large Australian min erals, resources, and steel processing company.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 220 pp. Englisch.

Codice articolo 9781461272090

Dati bibliografici

• Titolo: Robust Kalman Filtering for Signals and Systems with Large Uncertainties
• Editore: Birkhäuser Boston, Birkhäuser Boston Nov 2012
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1461272092
• ISBN 13: 9781461272090
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 13 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 13 millimetri di profondità
• Peso: 341 grammi

Descrizione articolo

Riassunto

A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in­ terested in problems of robust filtering through his research collaboration with Dr. Duncan McFarlane. At this time, Dr. McFarlane was employed at the Melbourne Research Laboratories ofBHP Ltd. , a large Australian min­ erals, resources, and steel processing company.

Recensione

"The book is primarily a research monograph which presents, in a unified fashion, some recent research on robust Kalman filtering. The book is intended for researchers in robust control and filtering theory, advanced postgraduate students, and engineers with an interest in applying the latest techniques of robust Kalman filtering. Robust Kalman filtering extends the Kalman filtering and the extended Kalman filtering to systems that contain uncertain parameters in addition to the usual white Gaussian noise.... Several examples are given, showing the robust Kalman filters outperforming the regular Kalman filter or the extended Kalman filter. Each of the first ten chapters covers a specific topic, usually with a major theorem characterizing the robust filter followed by an example. The final chapter addresses its application to a particular problem." ―Zentralblatt Math