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Bootstrap control

Aronsson, M ; Arvastson, Lars LU ; Holst, Jan LU ; Lindoff, Bengt LU and Svensson, A (2006) In IEEE Transactions on Automatic Control 51(1). p.28-37
Abstract
In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
control, stochastic control, statistical process, statistical bootstrap techniques, resampling, quality control, generalized predictive control, optimal control
in
IEEE Transactions on Automatic Control
volume
51
issue
1
pages
28 - 37
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000234724300003
  • scopus:31344448185
ISSN
0018-9286
DOI
10.1109/TAC.2005.861722
language
English
LU publication?
yes
id
1869df6f-3adf-4dd3-8f7b-4cd683f65641 (old id 419855)
date added to LUP
2016-04-01 15:41:07
date last changed
2022-01-28 06:33:04
@article{1869df6f-3adf-4dd3-8f7b-4cd683f65641,
  abstract     = {{In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.}},
  author       = {{Aronsson, M and Arvastson, Lars and Holst, Jan and Lindoff, Bengt and Svensson, A}},
  issn         = {{0018-9286}},
  keywords     = {{control; stochastic control; statistical process; statistical bootstrap techniques; resampling; quality control; generalized predictive control; optimal control}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{28--37}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Automatic Control}},
  title        = {{Bootstrap control}},
  url          = {{http://dx.doi.org/10.1109/TAC.2005.861722}},
  doi          = {{10.1109/TAC.2005.861722}},
  volume       = {{51}},
  year         = {{2006}},
}