Robust Kalman Filtering for Signals and Systems with Large Uncertainties

1. Front Matter

   Pages i-x

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2. Introduction

   • Ian R. Petersen, Andrey V. Savkin

   Pages 1-9

3. Continuous-Time Quadratic Guaranteed Cost Filtering

   • Ian R. Petersen, Andrey V. Savkin

   Pages 11-34

4. Discrete-Time Quadratic Guaranteed Cost Filtering

   • Ian R. Petersen, Andrey V. Savkin

   Pages 35-55

5. Continuous-Time Set-Valued State Estimation and Model Validation

   • Ian R. Petersen, Andrey V. Savkin

   Pages 57-69

6. Discrete-Time Set-Valued State Estimation

   • Ian R. Petersen, Andrey V. Savkin

   Pages 71-87

7. Robust State Estimation with Discrete and Continuous Measurements

   • Ian R. Petersen, Andrey V. Savkin

   Pages 89-105

8. Set-Valued State Estimation with Structured Uncertainty

   • Ian R. Petersen, Andrey V. Savkin

   Pages 107-129

9. Robust H∞ Filtering with Structured Uncertainty

   • Ian R. Petersen, Andrey V. Savkin

   Pages 131-144

10. Robust Fixed Order H∞ Filtering

    • Ian R. Petersen, Andrey V. Savkin

    Pages 145-151

11. Set-Valued State Estimation for Nonlinear Uncertain Systems

    • Ian R. Petersen, Andrey V. Savkin

    Pages 153-172

12. Robust Filtering Applied to Induction Motor Control

    • Ian R. Petersen, Andrey V. Savkin

    Pages 173-183

13. Erratum

    • Ian R. Petersen, Andrey V. Savkin

    Pages 201-207

14. Back Matter

    Pages 185-200

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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.

• Kalman filter
• control engineering
• design
• development
• filter
• filtering
• filters
• signal processing
• stability
• systems and signals

"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

• School of Electrical Engineering Australian Defence Force Academy, University of New South Wales, Canberra, Australia

  Ian R. Petersen

• Department of Electrical and Electronic Engineering, University of Western Australia, Nedlands, Australia

  Andrey V. Savkin

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