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Distributed Model Predictive Control for UAVs Collaborative Payload Transport

2020· article· en· W3132087422 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
TopicAdvanced Control Systems Optimization
Canadian institutionsMcGill University
FundersNCR
KeywordsPayload (computing)Control theory (sociology)ComputationController (irrigation)TrajectoryModel predictive controlComputer scienceControl engineeringVehicle dynamicsEngineeringControl (management)Aerospace engineeringAlgorithmPhysicsArtificial intelligenceComputer network

Abstract

fetched live from OpenAlex

We consider the problem of collaborative transport of a payload using several quadrotor vehicles. The payload is assumed to be a rigid body and is attached to the vehicles with rigid rods. The model of the system is presented and is employed to formulate a Model Predictive Controller. The centralized MPC formulation differs from others in the literature in the way the linearized model of the system is employed about a non-equilibrium state-input pair. We then present a decentralized formulation of MPC by distributing the computations among the vehicles. Simulations of both versions of the controller are carried out for a four-quadrotor system carrying out a transport maneuver of a box payload, for a cost penalizing the deviations of the vehicles from the desired trajectory and the attitude perturbations of the payload. The results confirm that the decentralized controller can yield a comparable performance to the centralized MPC implementation, for the same computation time of the two algorithms.

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: none
Teacher disagreement score0.988
Threshold uncertainty score0.608

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations50
Published2020
Admission routes1
Has abstractyes

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