Solving chance constrained optimal control problems in aerospace via Kernel Density Estimation

Abstract : The goal of this paper is to show how non-parametric statistics can be used to solve some chance constrained optimization and optimal control problems. We use the Kernel Density Estimation method to approximate the probability density function of a random variable with unknown distribution , from a relatively small sample. We then show how this technique can be applied and implemented for a class of problems including the God-dard problem and the trajectory optimization of an Ariane 5-like launcher.
Type de document :
Pré-publication, Document de travail
2017
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https://hal.inria.fr/hal-01507063
Contributeur : Jean-Baptiste Caillau <>
Soumis le : mercredi 12 avril 2017 - 14:50:05
Dernière modification le : lundi 24 avril 2017 - 10:10:20

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paper_kde.pdf
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  • HAL Id : hal-01507063, version 1

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Jean-Baptiste Caillau, Max Cerf, Achille Sassi, Emmanuel Trélat, Hasnaa Zidani. Solving chance constrained optimal control problems in aerospace via Kernel Density Estimation. 2017. <hal-01507063>

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