Robust design of a reentry unmanned space vehicle by multifidelity evolution control

Tools

Minisci, Edmondo and Vasile, Massimiliano (2013) Robust design of a reentry unmanned space vehicle by multifidelity evolution control. AIAA Journal, 51 (6). pp. 1284-1295. ISSN 0001-1452 (https://doi.org/10.2514/1.j051573)

[thumbnail of Minisci_E_Vasile_M_Pure_Robust_design_of_a_re_entry_unmanned_space_vehicle_by_multi_fidelity_evolution_control_Mar_2013.pdf] PDF. Filename: Minisci_E_Vasile_M_Pure_Robust_design_of_a_re_entry_unmanned_space_vehicle_by_multi_fidelity_evolution_control_Mar_2013.pdf
Preprint
Download (762kB)

Abstract

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.

ORCID iDs

Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528 and Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465;
CORE (COnnecting REpositories)