Overview
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.
Full Product Details
Author: Ian Petersen (University College, University of New South Wales, Canberra, Australia) ,
Andrey Savkin (University of Western Australia, Nedlands, Australia) ,
William Levine
Publisher: Birkhauser Boston Inc
Imprint: Birkhauser Boston Inc
Dimensions:
Width: 15.50cm
, Height: 1.50cm
, Length: 23.50cm
Weight: 0.420kg
ISBN: 9780817640897
ISBN 10: 0817640894
Pages: 210
Publication Date: 10 November 1999
Audience:
College/higher education
,
Professional and scholarly
,
Postgraduate, Research & Scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Awaiting stock
The supplier is currently out of stock of this item. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out for you.
Reviews
"""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"
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
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
