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Record W7133018893

Comparison of Numerical Methods for Low-Thrust Spacecraft Trajectory Optimization

2024· dissertation· W7133018893 on OpenAlexaff
Sanjeev Narayanaswamy

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTrajectoryTrajectory optimizationControl theory (sociology)SpacecraftOrbital maneuverOrbit (dynamics)RendezvousThrustOptimal control
DOInot available

Abstract

fetched live from OpenAlex

Low-thrust spacecraft propulsion systems enable fuel-efficient trajectories through space but the resulting trajectory optimization problems can be challenging. In this work, various numerical approaches for designing such low-thrust trajectories have been analyzed and compared. First, the Hermite-Legendre-Gauss-Lobatto (HLGL) and the Legendre-Gauss pseudospectral (PS) direct collocation methods, which are used for transcribing an optimal control problem into a nonlinear programming problem, have been compared for a minimum-time low-thrust Earth-to-Mars transfer problem. Next, a novel control law, the RQ-Law, is presented for generating low-thrust three-dimensional multi-revolution coasting-enabled rendezvous trajectories with a moving target, based on modified equinoctial elements. It builds upon the Q-Law, which is a Lyapunov feedback control law for orbital transfers. The RQ-Law offers an alternate method of determining the Lyapunov-optimal thrust angles used for both orbital transfer and phasing. It also provides a new target semimajor axis augmentation scheme that is demonstrated to perform phasing in a wide range of eccentric orbits. Compared with existing low-thrust Lyapunov rendezvous methods, the RQ-Law can include coasting arcs in the trajectory to save fuel as well as account for a minimum periapsis radius constraint. Alongside a thorough qualitative comparison of the RQ-Law, the performance of this law is evaluated numerically by using it to generate a rendezvous trajectory involving large changes in all six orbital elements. This performance was compared with a hybrid control law composed of an existing modified equinoctial Q-Law and a spiral phasing law. Three trade studies were performed that studied, respectively, the effects of the chaser departure point, the point at which low-thrust phasing is initiated, and the target orbit eccentricity, on the RQ-Law performance. Finally, to investigate the use of the RQ-Law to help mitigate the problem of space debris, we develop a low-thrust multiple-rendezvous trajectory to traverse a predetermined sequence of targets. The RQ-Law advances the state-of-the-art of the Q-Law and provides an integrated design tool for preliminary low-thrust rendezvous trajectory generation without needing an initial guess. The contributions of this thesis can be used for a variety of planetocentric and interplanetary space missions that use low-thrust electric propulsion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.411
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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