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Record W4231684162 · doi:10.32920/ryerson.14662506.v1

Intelligent Control of Satellite Formation Flying

2021· preprint· en· W4231684162 on OpenAlexafffund
Junquan Li

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsToronto Metropolitan University
FundersYork University
KeywordsRedundancy (engineering)Control theory (sociology)Fault detection and isolationReaction wheelActuatorSatelliteComputer scienceFault toleranceFault (geology)Attitude controlSliding mode controlControl engineeringEngineeringControl systemNonlinear systemReal-time computingControl (management)Reliability engineeringDistributed computingAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Small satellites flying in formation present a more efficient and affordable way of achieving the same or better performance than a large satellite because of low cost, high density of functionality and a short development cycle. A key technology for achieving mission objectives is the attitude and orbit control system. The overall objective of this dissertation research focuses on developing advanced control strategies and fault tolerant control for satellite formation flying. It is necessary to design and operate the satellite formation flying system to reduce fuel consumption and improve control accuracy. This is a very challenging task due to the nonlinear nature of satellite formation dynamics and the risk of thrusters’ failures and sensors’ faults in the absence of hardware redundancy. A class of nonlinear leader-follower satellite formation flying systems subject to uncertain thrusters’ and sensors’ faults and external J2 disturbances has been studied applying fault detection and identification and second order sliding mode control methodologies. New fault detection and identification and fault tolerant control algorithms were compared with model based fault detection and identification and fault tolerant control algorithms in presence of large initial errors, timevarying external disturbances, and parameter uncertainties. The faults considered were modeled as constant or ramp faults. Numerical results demonstrated the effectiveness of the proposed active fault tolerant control under actuators’ and sensors’ faults. It has been shown that the proposed second order sliding mode control scheme can guarantee local asymptotic stability after system faults. Simulation results confirmed that the suggested control methodologies yield high formation keeping precision and effectiveness for leaderfollower formation flying systems. The tracking errors of the proposed second order sliding mode control, adaptive fuzzy sliding mode control, chattering free sliding mode control and classic sliding mode control resulting from the thruster faults are within 2 m, 4 m, 10 m and 1 m, respectively. The fuel consumption of the proposed second order sliding mode control was the least. It is also necessary to design a fault tolerant satellite attitude control system to reduce fuel consumption and improve control performance accuracy. The proposed fault tolerant attitude control algorithms were based on first order and higher order sliding mode control theory as well as fuzzy logic systems to achieve real time autonomous fault tolerant control. These algorithms were applied to attitude synchronization in both leader-follower formation flying and decentralized formation flying. Attitude synchronization during formation flying was examined considering actuator dynamics while decentralized attitude ynchronization was studied using graph theory with quaternion kinematics. The proposed fault tolerant control algorithm was compared with the existing satellite attitude system controllers in the literature and it was found that the proposed algorithm resulted in three axis attitude stabilization within 0.041◦ in all axes for the fault cases. The reaction wheels’ Coulomb friction, saturations, noise, dead-zones, bias fault and external disturbances are considered. Finally, a nonlinear adaptive fuzzy sliding mode controller was tested using embedded nanosatellite hardware on a frictionless spherical air bearing system. The test results showed attitude errors of 0.8◦ using the proposed controller while a proportional integral derivative controller resulted in 5◦ attitude errors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.212
Teacher spread0.200 · 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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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