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Record W2321545702 · doi:10.2514/6.2005-6259

Noncollocated Position Sensor Effect on Flexible Robot Control

2005· article· en· W2321545702 on OpenAlex
Anthony R. Green, Jurek Z. Sąsiadek

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

VenueAIAA Guidance, Navigation, and Control Conference and Exhibit · 2005
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsPosition (finance)RobotComputer scienceControl (management)Robot controlComputer visionMobile robotArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Tracking a flexible robot is simulated using nonadaptive and fuzzy logic system (FLS) adaptive control strategies with noncollocated joint rotation sensors then with a position sensor located 0.5m, 2.25m and 4.0m from the end effector. Collocated joint rotation sensors satisfy hyperstability conditions but fail to capture nonminimum phase (NMP) response that causes control action delay. A position sensor noncollocated at the end effector captures NMP response. Results are poor for nonadaptive control with a collocated rotation sensor and a noncollocated end effector position sensor but very good results are achieved for FLS adaptive control. For noncollocated sensors at 0.5m, 2.25m and 4.0m from the end effector, tracking control results for both strategies are similar but drastically deviate from the commanded trajectory as the sensor gets closer to the elbow joint. FLS adaptive control is most effective for a sensor located either at the elbow joint or end effector.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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