Polynomial representation of model uncertainty in dynamical systems

Massimiliano Vasile

doi:10.1007/978-3-319-89988-6_25

Polynomial representation of model uncertainty in dynamical systems

Massimiliano Vasile

School Of Psychological Sciences And Health

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Citations (Scopus)

30 Downloads (Pure)

Abstract

This chapter introduces an approach to capture unmodelled components in dynamical systems through a hierarchical polynomial expansion in the state space. This approach is reminiscent of the empirical acceleration approach commonly used in precise orbit determination to account for unmodelled components in the force model.

Original language	English
Title of host publication	Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences
Subtitle of host publication	Computational Methods in Applied Sciences
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	419-432
Number of pages	14
ISBN (Print)	9783319899862
DOIs	https://doi.org/10.1007/978-3-319-89988-6_25
Publication status	Published - 1 Aug 2019

Publication series

Name	Computational Methods in Applied Sciences
Volume	48
ISSN (Print)	1871-3033

Keywords

dynamical systems
hierarchical polynomial expansion
uncertainty
space missions
astronautics

Access to Document

10.1007/978-3-319-89988-6_25

Vasile-CMAS-2019-Polynomial-representation-of-model-uncertainty-in-dynamical-systemsAccepted author manuscript, 263 KB

Marie Curie ITN (Stardust)
Vasile, M., Biggs, J., Burns, D., Hopkins, J., Macdonald, M., McInnes, C., Minisci, E. & Maddock, C.
European Commission - FP7 - General
1/02/13 → 31/01/17
Project: Research

Cite this

Vasile, M. (2019). Polynomial representation of model uncertainty in dynamical systems. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences: Computational Methods in Applied Sciences (pp. 419-432). (Computational Methods in Applied Sciences; Vol. 48). Springer. https://doi.org/10.1007/978-3-319-89988-6_25

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keywords = "dynamical systems, hierarchical polynomial expansion, uncertainty, space missions, astronautics",

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doi = "10.1007/978-3-319-89988-6_25",

language = "English",

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Vasile, M 2019, Polynomial representation of model uncertainty in dynamical systems. in Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences: Computational Methods in Applied Sciences. Computational Methods in Applied Sciences, vol. 48, Springer, Cham, Switzerland, pp. 419-432. https://doi.org/10.1007/978-3-319-89988-6_25

Polynomial representation of model uncertainty in dynamical systems. / Vasile, Massimiliano.

Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences: Computational Methods in Applied Sciences. Cham, Switzerland : Springer, 2019. p. 419-432 (Computational Methods in Applied Sciences; Vol. 48).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Polynomial representation of model uncertainty in dynamical systems

AU - Vasile, Massimiliano

PY - 2019/8/1

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N2 - This chapter introduces an approach to capture unmodelled components in dynamical systems through a hierarchical polynomial expansion in the state space. This approach is reminiscent of the empirical acceleration approach commonly used in precise orbit determination to account for unmodelled components in the force model.

AB - This chapter introduces an approach to capture unmodelled components in dynamical systems through a hierarchical polynomial expansion in the state space. This approach is reminiscent of the empirical acceleration approach commonly used in precise orbit determination to account for unmodelled components in the force model.

KW - dynamical systems

KW - hierarchical polynomial expansion

KW - uncertainty

KW - space missions

KW - astronautics

U2 - 10.1007/978-3-319-89988-6_25

DO - 10.1007/978-3-319-89988-6_25

M3 - Chapter

AN - SCOPUS:85049509191

SN - 9783319899862

T3 - Computational Methods in Applied Sciences

SP - 419

EP - 432

BT - Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

PB - Springer

CY - Cham, Switzerland

ER -

Vasile M. Polynomial representation of model uncertainty in dynamical systems. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences: Computational Methods in Applied Sciences. Cham, Switzerland: Springer. 2019. p. 419-432. (Computational Methods in Applied Sciences). Epub 2018 Jul 3. doi: 10.1007/978-3-319-89988-6_25

Polynomial representation of model uncertainty in dynamical systems

Abstract

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Keywords

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Projects

Marie Curie ITN (Stardust)

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