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Stochastic Optimal Trajectory Generation via Multivariate Polynomial Chaos

Whittle, Lisa (2017) Stochastic Optimal Trajectory Generation via Multivariate Polynomial Chaos. Master's, Institute of Space Systems.

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Abstract

This thesis presents a framework that has been developed in order to compute stochastic optimal trajectories. This is achieved by transforming the initial set of stochastic ordinary differential equations into their deterministic equivalent by application of Multivariate Polynomial Chaos. Via Galerkin projection, it is possible to include stochastic information in the optimal-trajectory generation process, and to solve the corresponding optimal-control problem using pseudospectral methods. The resultant trajectory is therefore less sensitive to the uncertainties included in the analysis, e.g., those present in system parameters, initial conditions or path constraints. The accurate, yet computationally efficient manner in which solutions are obtained is presented and a comparison with deterministic results show the benefits of the proposed approach for a variety of numerical examples

Item URL in elib:https://elib.dlr.de/121957/
Document Type:Thesis (Master's)
Title:Stochastic Optimal Trajectory Generation via Multivariate Polynomial Chaos
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Whittle, LisaInstitute of Space SystemsUNSPECIFIED
Date:September 2017
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:87
Status:Published
Keywords:Optimal Control, Stochastic Optimization, Polynomial Chaos, Pseudospectral Methods
Institution:Institute of Space Systems
Department:Guidance, Navigation, and Control
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transport
DLR - Research area:Raumfahrt
DLR - Program:R RP - Raumtransport
DLR - Research theme (Project):Re-usability Flight Experiment (ReFEx)
Location: Bremen
Institutes and Institutions:Institute of Space Systems > Navigation and Control Systems
Deposited By: Sagliano, Marco
Deposited On:02 Oct 2018 12:26
Last Modified:31 Jul 2019 20:19

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