An Overview of X-TFC Applications for Aerospace Optimal Control Problems

Enrico Schiassi, Andrea D’Ambrosio, Roberto Furfaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper is an overview of Optimal Control Problems (OCPs) for aerospace applications tackled via the indirect method and a particular Physics-Informed Neural Networks (PINNs) framework, developed by the authors, named Extreme Theory of Functional Connections (X-TFC). X-TFC approximates the unknown OCP solutions via the Constrained Expressions, which are functionals made up of the sum of a free-function and a functional that analytically satisfies the boundary conditions. Thanks to this property, the framework is fast and accurate in learning the solution to the Two-Point Boundary Value Problem (TPBVP) arising after applying the Pontryagin Minimum Principle. The applications presented in this paper regard intercept problems, interplanetary planar orbit transfers, transfer trajectories within the Circular Restricted Three-Body Problem, and safe trajectories around asteroids with collision avoidance. The main results are presented and discussed, proving the efficiency of the proposed framework in solving OCPs and its low computational times, which can potentially enable a higher level of autonomy in decision-making for practical applications.

Original languageEnglish (US)
Title of host publicationThe Use of Artificial Intelligence for Space Applications - Workshop at the 2022 International Conference on Applied Intelligence and Informatics
EditorsCosimo Ieracitano, Nadia Mammone, Marco Di Clemente, Mufti Mahmud, Roberto Furfaro, Francesco Carlo Morabito
PublisherSpringer Science and Business Media Deutschland GmbH
Pages199-212
Number of pages14
ISBN (Print)9783031257544
DOIs
StatePublished - 2023
Event2nd International Conference on Applied Intelligence and Informatics , AII 2022 - Reggio Calabria, Italy
Duration: Sep 1 2022Sep 3 2022

Publication series

NameStudies in Computational Intelligence
Volume1088
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference2nd International Conference on Applied Intelligence and Informatics , AII 2022
Country/TerritoryItaly
CityReggio Calabria
Period9/1/229/3/22

Keywords

  • Aerospace optimal control problems
  • Extreme learning machines
  • Machine learning
  • Physics-informed neural networks

ASJC Scopus subject areas

  • Artificial Intelligence

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