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Record W2887581072 · doi:10.1109/civemsa.2018.8440003

Sliding Mode Controller and Hierarchical Perturbation Compensator in a UAV Quadrotor

2018· article· en· W2887581072 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsControl theory (sociology)Perturbation (astronomy)Computer scienceInertiaControl engineeringEngineeringArtificial intelligencePhysicsControl (management)

Abstract

fetched live from OpenAlex

The commercial small Unmanned Aerial Vehicle (UAV) quadrotor is very sensitive to perturbation due to its relatively small size and because of being an under-actuated system. In general, some non-modeled parameters, wind disturbance, sensor noise and miscellaneous uncertainties cannot be easily quantified in UAV quadrotor systems. Changes of mass and inertia parameters for pick and place operations add more uncertainties to the system. Traditional controllers might not be robust enough to handle all aforementioned perturbation types. This arises the need of some perturbation compensation for guidance and stability. In this article, the complete system is synthesized as a combination of Hierarchical Perturbation Compensator (HPC) and a Sliding Mode Controller (SMC). With this compensation system, better tracking performance is demonstrated through analysis and simulation. The simulation response shows enhanced performance compared with other conventional methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.413

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 integrity0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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