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Record W2799340506 · doi:10.1139/tcsme-2017-0006

Simplification of the dynamic model of a hydraulic rockbreaker for implementation in a model-based control scheme

2018· article· en· W2799340506 on OpenAlexaffvenue
Louis-Francis Y. Tremblay, Marc Arsenault, Meysar Ƶeinali

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsLaurentian University
Fundersnot available
KeywordsExcavatorControl theory (sociology)ActuatorRobustness (evolution)Computer scienceControl engineeringHydraulic cylinderController (irrigation)EngineeringControl (management)Mechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A model-based approach to control the automation of hydraulic excavators and rockbreakers necessitates an adequate dynamic model to increase the robustness of the controller and improve its performance (e.g., reduce tracking error). Most previous efforts in developing dynamic models for excavators have assumed planar motion while neglecting the dynamic effects of hydraulically driven prismatic actuators. In this paper, a dynamic model of the mechanical subsystem of a hydraulic rockbreaker is developed using the Euler–Lagrange formulation. The model considers the contributions of the hydraulic actuators and does not assume planar motion. Potential simplifications to the dynamic model are then introduced to facilitate the model’s parameterization for developing an adaptive control algorithm. To evaluate their level of accuracy, these simplified dynamic models are then evaluated based on the required joint torques for specified trajectories. It is shown that the proposed simplifications reduce the complexity of the dynamic model while preserving its accuracy, which is attractive for real-time control applications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.985

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 head0.011
GPT teacher head0.231
Teacher spread0.219 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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