Book chapter
On line model uncertainty quantification: Hard upper bounds and convergence
The Modeling of Uncertainty in Control Systems, pp.197-219
Lecture Notes in Control and Information Sciences, Springer Berlin Heidelberg
06/22/2005
DOI: 10.1007/BFb0036261
Abstract
This paper considers the problem of on line uncertainty bound quantification in identification of restricted complexity models. Algorithms are presented, which provide hard and tight upper bound on the unknown model uncertainty in H2, H∞ and pointwise sense respectively. The algorithms proposed are very simple, on line and recursive. This allows robust control and adaptive identification to be combined.
Details
- Title: Subtitle
- On line model uncertainty quantification: Hard upper bounds and convergence
- Creators
- Er-Wei BaiSundar Raman - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- The Modeling of Uncertainty in Control Systems, pp.197-219
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Control and Information Sciences
- DOI
- 10.1007/BFb0036261
- eISSN
- 1610-7411
- ISSN
- 0170-8643
- Language
- English
- Date published
- 06/22/2005
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197922602771
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