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Record W4289828924 · doi:10.1109/med54222.2022.9837234

Comparison of Cellular Network Controllers for Quadrotors Experiencing Time Delay

2022· article· en· W4289828924 on OpenAlex
Mohammad Tayefe Ramezanlou, Howard M. Schwartz, Ioannis Lambadaris, Michel Barbeau

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

Venue2022 30th Mediterranean Conference on Control and Automation (MED) · 2022
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
FundersMitacs
KeywordsEstimatorControl theory (sociology)BacksteppingController (irrigation)Computer scienceNonlinear systemState (computer science)MathematicsControl (management)Adaptive controlAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a control framework including delay estimator, state estimator, and controller to compensate for random time delays in cellular networks. The effect of network delay on the control of a quadrotor is investigated. A comparative study is done between a linear-based PD and nonlinear Backstepping controller. A time delay estimator based on the Markov stochastic model is developed. The combination of the time delay estimator and the state estimator is used to compute the control signal. Results show that the performance of both controllers in low-variation delays is approximately equivalent. According to the results, the linear-based PD controller is a good choice since it satisfies the problem conditions with a more straightforward design process.

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)0.001
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: none
Teacher disagreement score0.945
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.304
Teacher spread0.254 · 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