Robust Kalman Filtering for Signals and Systems With Large Uncertainties

Petersen, Ian Richard; Savkin, Andrey V.

ISBN 10: 0817640894 / ISBN 13: 9780817640897

Verlag: Birkhäuser, 1999

Beschreibung

Unread book in perfect condition.

Bestandsnummer des Verkäufers 206880

Bibliografische Details

• Titel: Robust Kalman Filtering for Signals and Systems With Large Uncertainties
• Verlag: Birkhäuser
• Erscheinungsjahr: 1999
• Sprache: Englisch
• ISBN-10: 0817640894
• ISBN-13: 9780817640897
• Einband: Hardcover
• Zustand: As New

Über diesen Titel

Inhaltsangabe

1 Introduction.- 2 Continuous-Time Quadratic Guaranteed Cost Filtering.- 3 Discrete-Time Quadratic Guaranteed Cost Filtering.- 4 Continuous-Time Set-Valued State Estimation and Model Validation.- 5 Discrete-Time Set-Valued State Estimation.- 6 Robust State Estimation with Discrete and Continuous Measurements.- 7 Set-Valued State Estimation with Structured Uncertainty.- 8 Robust H? Filtering with Structured Uncertainty.- 9 Robust Fixed Order H? Filtering.- 10 Set-Valued State Estimation for Nonlinear Uncertain Systems.- 11 Robust Filtering Applied to Induction Motor Control.- References.

Críticas

"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