Discrete H∞ estimator design of unknown input: Game-theoretic approach

Rho, Hian; Kang, Yoon-Bok; Hyun, Seung-Ho; Kim, Hansil

doi:10.1016/j.sigpro.2010.11.003

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The method of combining multiple filters according to an innovation-based criterion is the key of our estimation method. The Kalman filtering algorithm, which is one of the most popular algorithms [23,63,100,112], is to effectively reduce Gaussian white noises, while the H ∞ filtering one [33,85], which has been becoming popular, e.g., [6,27,47,49,59,78,106], can accommodate norm-bounded deterministic noises, and therefore is expected to be robust against model errors. Our method of combining filters is to utilize the advantage of each filter as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Robust Motion Control of Oscillatory-Base Manipulators

Toda¹

2016

Lecture Notes in Control and Information Sciences

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About this SeriesThis series aims to report new developments in the fields of control and information sciences-quickly, informally and at a high level. The type of material considered for publication includes:1. Preliminary drafts of monographs and advanced textbooks 2. Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are (a) of exceptional interest and (b) devoted to a specific topic. The timeliness of subject material is very important. More information about this series atAdditional material to this book can be downloaded from http://extras.springer.com.

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