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Record W2158912029 · doi:10.1109/med.2013.6608928

Linear Model Predictive Control for the encirclement of a target using a quadrotor aircraft

2013· article· en· W2158912029 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
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsConcordia UniversityRoyal Military College of Canada
Fundersnot available
KeywordsMATLABController (irrigation)Computer scienceControl theory (sociology)Path (computing)Task (project management)TrajectoryControl (management)Control engineeringSimulationEngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Encirclement is a task accomplished by an Unmanned Aerial Vehicle (UAV) in order to maintain awareness and containment of a given target. The aim of the UAV encircling this target is to maintain close proximity at all times. In this paper, the problem of maintaining a circular path around a target is considered and a Linear Model Predictive Control (LMPC) strategy is implemented on a Qball-X4 quadrotor aircraft in order to follow the circular path. A linear model for the two-dimensional movement of the UAV and its respective MP controller has been designed in MATLAB Simulink, simulated in a X-Plane/MATLAB interface and implemented on the actual vehicle in real-time. The results of the LMPC in simulation are compared to those found while implementing the algorithm on a physical platform. The contributions of this paper lay in the implementation of an autonomous Linear MP controller for the encirclement of a stationary target by a Qball-X4 quadrotor.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.326
Threshold uncertainty score0.300

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 science0.0010.000
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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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