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Record W3011652201 · doi:10.48550/arxiv.2003.05992

Comments on `Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot'

2020· preprint· en· W3011652201 on OpenAlexaff
Mohammad Biglarbegian

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOmnidirectional antennaCompensation (psychology)Model predictive controlMobile robotControl (management)RobotComputer scienceControl engineeringControl theory (sociology)EngineeringArtificial intelligencePsychologyTelecommunicationsSocial psychology

Abstract

fetched live from OpenAlex

There are errors in the dynamics model in \cite{b1}. In addition, some details of the derivations and assumptions are missing in the paper. This letter was submitted to the IEEE Transactions on Mechatronics and although reviewers acknowledged the merits of the paper, but was not finally approved to be published and suggested to be submitted as a conference paper. I still think this work is worth disseminating and it is potentially very useful for students or practitioners. In this letter, (i) the assumptions made are presented and the governing dynamics with details are derived, and (ii) the correct equations followed by the correct component of the state-space model ($\boldsymbol{A}$) are given.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score1.000

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 head0.041
GPT teacher head0.201
Teacher spread0.160 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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