Stochastic Optimal Control under Non-Gaussian Uncertainties via Entropy Minimization and Dynamical Indicators

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robust and optimal trajectory planning in the face of nonlinear dynamics and non-gaussian uncertainties poses a fundamental challenge in the field of astronautics. This research paper delves into the exploration of entropy and dynamical indicators associated with Lagrangian Coherent Structures (LCS), specifically the Finite-Time Lyapunov Exponent (FTLE) and PseudoDiffusion Exponent, for addressing optimal control problems amidst uncertainties characterized by non-Gaussian probabilities. To accomplish this, sampling-based approaches and the Perron-Frobenius transfer operator are employed to generate open-loop stochastic optimal controls. Sparse grids are utilized to generate trajectory samples and reformulate stochastic optimal control as a deterministic optimization problem across the domain of uncertainty. Subsequently, SC-EPOCS is employed to tackle the resulting optimization problem and a comparative analysis of maximum terminal altitude and covariances is conducted to assess robustness exhibited by each metric in the context of trajectory planning for a Mars entry problem, considering uncertainties in both the initial state and model parameters.

Original languageEnglish
Title of host publicationAIAA SciTech Forum and Exposition, 2024
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624107115
DOIs
Publication statusPublished - 2024
EventAIAA SciTech Forum and Exposition, 2024 - Orlando, United States
Duration: 8 Jan 202412 Jan 2024

Publication series

NameAIAA SciTech Forum and Exposition, 2024

Conference

ConferenceAIAA SciTech Forum and Exposition, 2024
Country/TerritoryUnited States
CityOrlando
Period8/01/2412/01/24

Bibliographical note

Publisher Copyright:
© 2024 by Akan Selim.

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