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Record W2093175238 · doi:10.1115/imece2002-32408

Control of Flexible Payloads Grasped by Actuated Grippers Undergoing Large Rigid-Body Motions: Part II — Controllability and Observability

2002· article· en· W2093175238 on OpenAlexaff
Edward J. Park, James K. Mills

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControllabilityObservabilityControl theory (sociology)Payload (computing)GrippersActuatorRigid bodyVibrationVibration controlControl engineeringComputer scienceConstraint (computer-aided design)Controller (irrigation)EngineeringControl (management)PhysicsMathematicsMechanical engineeringArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

Part I of this work models the dynamics of a flexible payload grasped by an actuated gripper undergoing large rigid body motion by a robotic manipulator. In Part II, the controllability and observability conditions of the system are discussed. In Part I, the dynamic model of the actuated flexible payload is derived using the component mode synthesis (CMS) method with addition of actuator constraint, fixed-interface vibration and quasi-static modes. Here, the two-time scale modeling (TSM) technique is employed taking advantage of the two-time scale behavior between the quasi-static modes and vibration modes in the dynamic model. Due to the complexity of the resulting system, the controllability and observability conditions are not trivial. Hence, the controllability and observability study addressed herein becomes essential in showing the advantages of using the CMS and TSM techniques in control system design for the problem. A simulation example demonstrates that simultaneous vibration and quasi-static deformation control is achievable by proper selection of each type of modes.

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)0.001
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: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.938

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2002
Admission routes1
Has abstractyes

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