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

Abstract : The goal of this paper is to show how non-parametric statistics can be used to solve chance-constrained optimization and optimal control problems by reformulating them into deterministic ones, focusing on the details of the algorithmic approach. 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. In the paper it is shown how this technique can be applied to a class of chance-constrained optimization problem, focusing on the implementation of the method. In particular, in our examples we analyze a chance-constrained version of the well known problem in aerospace optimal control: the Goddard problem.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.inria.fr/hal-01406266
Contributeur : Jean-Baptiste Caillau <>
Soumis le : mercredi 30 novembre 2016 - 22:27:02
Dernière modification le : samedi 18 février 2017 - 01:20:08

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  • HAL Id : hal-01406266, version 1

Citation

Jean-Baptiste Caillau, Max Cerf, Achille Sassi, Emmanuel Trélat, Hasnaa Zidani. Solving chance-constrained optimal control problems in aerospace engineering via Kernel Density Estimation. 2016. <hal-01406266>

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