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Record W3135995516 · doi:10.1109/tie.2021.3065613

Closed-Loop Identification and Real-Time Control of a Micro Quadcopter

2021· article· en· W3135995516 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 · 2021
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuadcopterControl theory (sociology)BacksteppingController (irrigation)Computer scienceControl engineeringLinear-quadratic regulatorAttitude controlEngineeringAdaptive controlControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This article presents a novel model-based control design scheme for a commercially-available micro quadcopter. A simple yet effective attitude dynamics model was identified using the closed-loop instrumental variable method with an iterative minimization search algorithm, since the actual model of the onboard attitude controller was unknown. Model-based integral-linear quadratic regulator, backstepping, and nested saturation controllers were proposed and compared for hovering control of the micro quadcopter based on the identified attitude dynamics model and its theoretical translational model. The proposed hovering controllers were first evaluated in simulations and then validated experimentally. For the conditions used in this research, it was observed that the nested saturation controller generally had the best performance among the three proposed controllers when hovering in the presence of external wind disturbances, while the backstepping controller exhibited the worst performance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score0.801

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 head0.016
GPT teacher head0.223
Teacher spread0.208 · 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