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Record W2974064214 · doi:10.1051/jnwpu/20193730636

Quick Suppression of Vibration of Robot via Hybrid Input Shaping Control Strategy

2019· article· en· W2974064214 on OpenAlexaff
Yulan Wei, Bing Li, Qingzhu Zhang, Pengfei Ou

Bibliographic record

VenueXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University · 2019
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsModalInput shapingVibrationControl theory (sociology)Vibration controlModal analysis using FEMModal testingResidualComputer scienceModal analysisFrequency responseBandwidth (computing)Mode (computer interface)EngineeringAcousticsControl (management)PhysicsArtificial intelligenceMaterials scienceAlgorithm

Abstract

fetched live from OpenAlex

When the vibration amplitude and resonant frequency bandwidth of each mode are different in the multiple-modal system, the response time of a system is increased but the residual vibration is effectively reduced by the positive impulses multiple-modal input shapers. However, a negative impulses hybrid multiple-modal input shaping method can solve those problems. The basic principle of this control strategy and a 3-DOF parallel robot were introduced. Six negative impulses hybrid input shapers to reduce vibration of the first two modes were constructed based on the robot. Using simulation methods, the response time and vibration suppression abilities of various negative impulses hybrid two-modal input shapers (NHTIS) were obtained by analyzing the vibration response curves of these input shapers, and comparing with positive and negative impulses two-modal input shapers, respectively. The results show that the NHTIS can improve the response speed of the system while significantly reducing the multiple-modal residual vibration.

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: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.966

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 communication0.0000.001
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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
Published2019
Admission routes1
Has abstractyes

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