Dissertation/Thesis Abstract

Minimum-fuel optimal trajectory for reusable first-stage rocket landing using Particle Swarm Optimization
by Anglim, Kevin Spencer G., M.S., California State University, Long Beach, 2016, 89; 10196557
Abstract (Summary)

Reusable Launch Vehicles (RLVs) present a more environmentally-friendly and cost-effective approach to accessing space when compared to traditional launch vehicles that are discarded after each flight. This paper studies the recyclable nature of RLVs by presenting a solution method for determining minimal-fuel optimal trajectories using principles from optimal control theory and Particle Swarm Optimization (PSO). The problem is formulated as a minimum-fuel, minimum-landing error powered descent problem where it is desired to move the RLV from a fixed set of initial conditions to three different sets of terminal conditions: an unspecified downrange landing, a specified downrange landing, and a return-to-launch-site landing. However, unlike other powered descent studies, this paper considers the highly nonlinear effects caused by atmospheric drag, which are often ignored for similar powered descent studies on the Moon or Mars. Rather than optimize the controls directly, the throttle control is assumed to be bang-off-bang with a predetermined thrust direction for each phase of flight. An overview of optimal control theory and particle swarm optimization is first presented. The PSO method is presented and verified in a one-dimensional comparison study, and is then applied to the two-dimensional cases, the results of which are illustrated.

Indexing (document details)
Advisor: Gao, Qingbin
Commitee: Besnard, Eric, Shankar, Praveen
School: California State University, Long Beach
Department: Mechanical and Aerospace Engineering
School Location: United States -- California
Source: MAI 56/02M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Aerospace engineering
Keywords: Minimum-fuel trajectory, Particle swarm optimization, Reusable rocket
Publication Number: 10196557
ISBN: 978-1-369-36816-1
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