Filtering Theory With Applications to Fault Detection, Isolation, and Estimation
The focus of this book is on filtering for linear processes, and its primary goal is to design filters from a class of linear stable unbiased filters that yield an estimation error with the lowest root-mean-square (RMS) norm. Various hierarchical classes of filtering problems are defined based on th...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Boston, MA
Birkhäuser
2007, 2007
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| Edition: | 1st ed. 2007 |
| Series: | Systems & Control: Foundations & Applications
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Preliminaries
- A special coordinate basis (SCB) of linear multivariable systems
- Algebraic Riccati equations and matrix inequalities
- Exact disturbance decoupling via state and full information feedback
- Almost disturbance decoupling via state and full information feedback
- Exact input-decoupling filters
- Almost input-decoupled filtering under white noise input
- Almost input-decoupled filtering without statistical assumptions on input
- Optimally (suboptimally) input-decoupling filtering under white noise input—H2 filtering
- Optimally (suboptimally) input-decoupled filtering without statistical information on the input-H? filtering
- Generalized H2 suboptimally input-decoupled filtering
- Generalized H? suboptimally input-decoupled filtering
- Fault detection, isolation, and estimation—exact or almost fault estimation
- Fault detection, isolation, and estimation—optimal fault estimation