Hassibi, Babak and Kailath, Thomas (1998) H^∞-optimality of H^2 predictors. In: Proceedings of the 37th IEEE Conference on Decision and Control, 1998. Vol.1. IEEE , Piscataway, NJ, pp. 626-631. ISBN 0-7803-4394-8. https://resolver.caltech.edu/CaltechAUTHORS:20150302-070441915
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Abstract
Given past observations of a process, {y_j,j<i}, suppose we are interested in constructing one-step-ahead predictors of y i, denoted by yˆ_(i|i-1). We show that, irrespective of whether the process {y_j} is stationary or non-stationary, or whether it is scalar- or vector-valued, the H^2 -optimal one-step-ahead predictor is also H^∞-optimal. Since the H^2 and H∞ paradigms represent fundamentally different approaches to estimation and control, the estimators and controllers obtained from each formalism have often drastically different performances with respect to the other criterion. Our result, however, provides a nontrivial example of when the two formalisms lead to the same optimal design.
| Item Type: | Book Section | |||||||||
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| Additional Information: | © 1998 IEEE. This wort was supported in part by DARPA through the Department of Air Force under contract F49620-95-1-0525-P00001 and by the Joint Service Electronics Program at Stanford under contract DAAH04-94-G-0058-P00003. | |||||||||
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| DOI: | 10.1109/CDC.1998.760752 | |||||||||
| Record Number: | CaltechAUTHORS:20150302-070441915 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20150302-070441915 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 55366 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Shirley Slattery | |||||||||
| Deposited On: | 06 Mar 2015 01:52 | |||||||||
| Last Modified: | 10 Nov 2021 20:45 |
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