Uncertainty quantification in control problems for flocking models

Giacomo Albi; Lorenzo Pareschi; Mattia Zanella

doi:10.1155/2015/850124

Uncertainty quantification in control problems for flocking models

Giacomo Albi, Lorenzo Pareschi, Mattia Zanella^*

^*Corresponding author for this work

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

48 Citations (Scopus)

Abstract

The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

Original language	English
Article number	850124
Journal	Mathematical Problems in Engineering
Volume	2015
DOIs	https://doi.org/10.1155/2015/850124
Publication status	Published - 1 Jun 2015

ASJC Scopus subject areas

General Mathematics
General Engineering

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10.1155/2015/850124Licence: CC BY

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title = "Uncertainty quantification in control problems for flocking models",

abstract = "The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.",

author = "Giacomo Albi and Lorenzo Pareschi and Mattia Zanella",

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T1 - Uncertainty quantification in control problems for flocking models

AU - Albi, Giacomo

AU - Pareschi, Lorenzo

AU - Zanella, Mattia

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N2 - The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

AB - The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

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DO - 10.1155/2015/850124

M3 - Article

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VL - 2015

JO - Mathematical Problems in Engineering

JF - Mathematical Problems in Engineering

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Uncertainty quantification in control problems for flocking models

Abstract

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