Improved sample complexity estimates for statistical learningcontrol of uncertain systems
Koltchinskii, V.
Abdallah, C.T.
Ariola, M.
Dorato, P.
Panchenko, D.
Dept. of Math. & Stat., New Mexico Univ., Albuquerque, NM;
This paper appears in: Automatic Control, IEEE Transactions on
Publication Date: Dec 2000
Volume: 45,
Issue: 12
On page(s): 2383-2388
ISSN: 0018-9286
References Cited: 28
CODEN: IETAA9
INSPEC Accession Number: 6828992
Digital Object Identifier: 10.1109/9.895579
Current Version Published: 2002-08-06
Abstract
Probabilistic methods and statistical learning theory have been
shown to provide approximate solutions to “difficult”
control problems. Unfortunately, the number of samples required in order
to guarantee stringent performance levels may be prohibitively large.
This paper introduces bootstrap learning methods and the concept of
stopping times to drastically reduce the bound on the number of samples
required to achieve a performance level. We then apply these results to
obtain more efficient algorithms which probabilistically guarantee
stability and robustness levels when designing controllers for uncertain
systems
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