Robust space trajectory design using belief stochastic optimal control

Cristian Greco, Stefano Campagnola, Massimiliano Vasile

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Abstract

This paper presents a belief-based formulation and a novel approach for the robust solution of optimal control problems under uncertainty. The introduced formulation, based on the Belief Markov Decision Process model, reformulates the control problem directly in terms of uncertainty distributions, called beliefs, rather than on realisations of the system state. Successively, an approach inspired by navigation analysis is developed to transcribe and solve such problem in the presence of observation windows, employing a polynomial expansion for the dynamical propagation. Finally, the developed method is applied to the robust optimisation of a flyby trajectory of Europa Clipper mission in a scenario characterised by knowledge, execution and observation errors.
Original languageEnglish
Pages1-25
Number of pages25
DOIs
Publication statusPublished - 5 Jan 2020
EventAmerican Institute of Aeronautics and Astronautics (AIAA) SciTech Forum
- Hyatt Regency Orlando , Orlando, United States
Duration: 6 Jan 202010 Jan 2020
https://www.aiaa.org/SciTech

Conference

ConferenceAmerican Institute of Aeronautics and Astronautics (AIAA) SciTech Forum
Abbreviated titleSciTech 2020
CountryUnited States
CityOrlando
Period6/01/2010/01/20
Internet address

Keywords

  • optimal control
  • Markov decision processes

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    Greco, C., Campagnola, S., & Vasile, M. (2020). Robust space trajectory design using belief stochastic optimal control. 1-25. Paper presented at American Institute of Aeronautics and Astronautics (AIAA) SciTech Forum
    , Orlando, United States. https://doi.org/10.2514/6.2020-1471