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Record W4226424875 · doi:10.22215/etd/2021-14824

An Evolutionary Framework for Multi-Objective Trajectory Design and Robust Model Predictive Control in Long-Range Rendezvous Missions

2021· dissertation· en· W4226424875 on OpenAlexafffund
Sanaz Samsam

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRendezvousControl theory (sociology)Trajectory optimizationModel predictive controlTrajectoryMathematical optimizationComputer scienceGenetic algorithmOptimization problemImpulse (physics)Multi-objective optimizationRange (aeronautics)Optimal controlEngineeringMathematicsControl (management)Artificial intelligenceSpacecraft

Abstract

fetched live from OpenAlex

This thesis presents an optimization architecture for on-orbit servicing mission design in the long-range rendezvous phase.We develop a methodology to generate Pareto Optimal trajectories for long-range rendezvous of a servicing satellite with a moving target.The methodology employs a multi-impulse shape-based trajectory planning algorithm for in-plane orbit transfer, based on the two-body problem.We first derive the necessary and sufficient conditions that determine the set of smooth impulsive trajectories connecting the servicing satellite to the orbiting target.The Pareto Optimal trajectories from this set are then obtained using a constrained multiobjective optimization algorithm developed based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II).Transfer time and control effort are the two Pareto cost functions that are considered in the multi-objective optimization.To reduce the risk of collision in populated orbits and to remain in an orbital regime, we include restrictions on orbital elements as part of the constraints.Further, a maximum available impulse is considered as an upper-bound for velocity changes in an impulsive trajectory.The number of impulses along with the location of the first impulse in the parking orbit and the orbital parameters of the intermediate orbits form the set of design variables.We demonstrate the superiority of the developed trajectory planner by comparing its results with those obtained from another multi-objective evolutionary algorithm called the Multi-Objective Genetic Algorithm and an optimal Lambert approach.In an on-orbit servicing mission, a solution from the Pareto frontier set of optimal trajectories may be selected based on the mission requirements.To robustly follow the generated reference trajectories in a J 2 -perturbed orbital environment, we propose a Nonlinear Model Predictive Control (NMPC) scheme.The control signals are velocity increments at the time of applying each impulse, and the variable horizon is considered to be the time difference between every two impulses in the reference trajectory.To avoid singularities, the equinoctial orbital elements are used in the process model that includes the secular and first-order long-periodic effects iii x List of Tables 6.1 Summary of the optimal results; Case Study 1 . . . . . . . . . . . . . . . . .

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
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)0.0010.000

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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
GenreEmpirical

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

Citations1
Published2021
Admission routes2
Has abstractyes

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