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Randomization methods in optimization and adaptive control - Dedicated to Tyrone Duncan on occasion of his 60th birthday
Computer and Automation Institute of the Hungarian Academy of Sciences, (MTA SZTAKI),.
KTH, Superseded Departments, Signals, Sensors and Systems. (Reglerteknik)ORCID iD: 0000-0002-9368-3079
2002 (English)In: STOCHASTIC THEORY AND CONTROL: Proceedings of a Workshop held in Lawrence, Kansas, Springer-Verlag New York, 2002, Vol. 280, 137-153 p.Chapter in book (Refereed)
Abstract [en]

We consider simultaneous perturbation stochastic approximation (SPSA) methods applied to noise-free problems in optimization and adaptive control. More generally, we consider discrete-time fixed gain stochastic approximation processes that are defined in terms of a random field that is identically zero at some point theta*. The boundedness of the estimator process is enforced by a resetting mechanism. Under appropriate technical conditions the estimator sequence converges to theta* with geometric rate almost surely. This result is in striking contrast to classical stochastic approximation theory where the typical convergence rate is n(-1/2). For the proof a discrete-time version of the ODE-method is used and the techniques of [10] are extended. A simple variant of noise free-SPSA is applied to extend a direct controller tuning method named Iterative Feedback Timing (IFT), see [16]. Using randomization, the number of experiments required to obtain an unbiased estimate of the gradient of the cost function can be reduced significantly for multi-input multi-output systems.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2002. Vol. 280, 137-153 p.
Series
LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES, ISSN 0170-8643
Keyword [en]
PERTURBATION GRADIENT APPROXIMATION, STOCHASTIC-APPROXIMATION, CONVERGENCE, ALGORITHM
National Category
Control Engineering
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-26598ISI: 000177472100011ISBN: 978-3-540-43777-0 OAI: oai:DiVA.org:kth-26598DiVA: diva2:376353
Conference
Workshop on Stochastic Theory and Control UNIV KANSAS, LAWRENCE, KS, OCT 18-20, 2001
Note
QC 20101210 NR 20140804Available from: 2010-12-10 Created: 2010-11-25 Last updated: 2012-01-13Bibliographically approved

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Hjalmarsson, Håkan

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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