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Record W2126006156 · doi:10.1109/acc.2001.946187

On derivation of constrained multiple rigid body dynamic equations

2001· article· en· W2126006156 on OpenAlexaff
R. F. Abo-Shanab, Q. Wu, Nariman Sepehri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConstraint (computer-aided design)State spaceState (computer science)Computer sciencePhysical systemSpace (punctuation)Mathematical optimizationState-space representationClass (philosophy)Control theory (sociology)Applied mathematicsMathematicsAlgorithmArtificial intelligenceGeometryPhysics

Abstract

fetched live from OpenAlex

Dynamic modeling of constrained multi-link systems are traditionally tackled either in the reduced state space or in the non-reduced state space. In this paper, an alternative approach for deriving dynamic equations for a class of multi-link constrained systems is proposed. The constraints, studied in this paper, are joints where the constraint forces are of interest. The method starts with replacing the joints with virtual links. Next, the augmented state space is formed by the states describing the original physical system and virtual links. The dynamic equations, describing the expanded systems, are then derived in the reduced state space from the viewpoint of the augmented state space. Finally, the dynamic equations for the original multi-link systems are obtained by setting all the physical parameters and states associated with virtual links to zero. This alternative approach has the advantages of both traditional methods in that: (i) the constraint forces can be determined directly, (ii) the violation of constraints is avoided, which is the main problem for dynamic modeling of constrained systems in the non-reduced state space, and (iii) all computer software packages for automatic generation of dynamic equations in the reduced state space can be readily used.

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 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 score0.837
Threshold uncertainty score0.250

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 head0.205
Teacher spread0.198 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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