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Record W2921509807 · doi:10.1109/tie.2019.2902789

Finite-Time Trajectory Tracking Control of Space Manipulator Under Actuator Saturation

2019· article· en· W2921509807 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)TrajectoryActuatorLyapunov functionController (irrigation)Payload (computing)Computer scienceSliding mode controlTerminal sliding modeControl engineeringEngineeringArtificial intelligenceControl (management)Nonlinear systemPhysics

Abstract

fetched live from OpenAlex

This paper proposes a finite-time trajectory tracking controller for a space manipulator under model uncertainty, external disturbance, and actuator saturation. The dynamics of space manipulator is derived using Kane's method. Considering the model uncertainty that may exist when the manipulator captures an unknown payload, a radial basis function neural network (NN) is used to estimate the uncertain model of the space manipulator. An auxiliary system is designed to compensate the actuator saturation. Then a NN-based adaptive terminal sliding mode controller is proposed for trajectory tracking of a space manipulator. The stability of the proposed controller is analyzed using Lyapunov theory. Numerical simulations are conducted to verify the effectiveness of the proposed controller.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.216
Teacher spread0.195 · 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