Bootstrap control

M Aronsson; Lars Arvastson; Jan Holst; Bengt Lindoff; A Svensson

doi:10.1109/TAC.2005.861722

Bootstrap control

M Aronsson, Lars Arvastson, Jan Holst, Bengt Lindoff, A Svensson

Mathematical Statistics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	28-37
Journal	IEEE Transactions on Automatic Control
Volume	51
Issue number	1
DOIs	https://doi.org/10.1109/TAC.2005.861722
Publication status	Published - 2006

Subject classification (UKÄ)

Probability Theory and Statistics

Free keywords

control
stochastic control
statistical process
statistical bootstrap techniques
resampling
quality control
generalized predictive control
optimal control

Access to Document

10.1109/TAC.2005.861722

Cite this

@article{1869df6f3adf4dd38f7b4cd683f65641,

title = "Bootstrap control",

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.",

keywords = "control, stochastic control, statistical process, statistical bootstrap techniques, resampling, quality control, generalized predictive control, optimal control",

author = "M Aronsson and Lars Arvastson and Jan Holst and Bengt Lindoff and A Svensson",

year = "2006",

doi = "10.1109/TAC.2005.861722",

language = "English",

volume = "51",

pages = "28--37",

journal = "IEEE Transactions on Automatic Control",

issn = "0018-9286",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

number = "1",

}

TY - JOUR

T1 - Bootstrap control

AU - Aronsson, M

AU - Arvastson, Lars

AU - Holst, Jan

AU - Lindoff, Bengt

AU - Svensson, A

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - control

KW - stochastic control

KW - statistical process

KW - statistical bootstrap techniques

KW - resampling

KW - quality control

KW - generalized predictive control

KW - optimal control

UR - https://www.scopus.com/pages/publications/31344448185

U2 - 10.1109/TAC.2005.861722

DO - 10.1109/TAC.2005.861722

M3 - Article

SN - 0018-9286

VL - 51

SP - 28

EP - 37

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

IS - 1

ER -

Bootstrap control

Abstract

Subject classification (UKÄ)

Free keywords

Access to Document

Other files and links

Fingerprint

Cite this