Dynamic Soaring in Uncertain Wind Conditions: Polynomial Chaos Expansion Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU149411" target="_blank" >RIV/00216305:26230/24:PU149411 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-53969-5_9" target="_blank" >http://dx.doi.org/10.1007/978-3-031-53969-5_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-53969-5_9" target="_blank" >10.1007/978-3-031-53969-5_9</a>

Result language

angličtina

Original language name

Dynamic Soaring in Uncertain Wind Conditions: Polynomial Chaos Expansion Approach

Original language description

Dynamic soaring refers to a flight technique used primarily by large seabirds to extract energy from the wind shear layers formed above ocean surface. A small Unmanned Aerial Vehicle (UAV) capable of efficient dynamic soaring maneuvers can enable long endurance missions in context of patrol or increased flight range. To realize autonomous energy-saving patterns by a UAV, a real-time trajectory generation for a dynamic soaring maneuver accounting for varying external conditions has to be performed. The design of the flight trajectory is formulated as an Optimal Control Problem (OCP) and solved within direct collocation based optimization. A surrogate model of the optimal traveling cycle capturing wind profile uncertainties is constructed using Polynomial Chaos Expansion (PCE). The unknown wind profile parameters are estimated from observed trajectory by means of a Genetic Algorithm (GA). The PCE surrogate model is subsequently utilized to update the optimal trajectory using the estimated wind profile parameters.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Machine Learning, Optimization, and Data Science

ISBN

978-3-031-53968-8

ISSN

0302-9743

e-ISSN

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Number of pages

11

Pages from-to

104-115

Publisher name

Springer Nature Switzerland AG

Place of publication

Grasmere

Event location

Grasmere

Event date

Sep 22, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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