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Tracking Control of Force, Position, and Contour for an Excavator with Co-simulation

2020· article· en· W3107350975 on OpenAlexaff
Niraj Reginald, Jaho Seo, Abdullah Rasul

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExcavatorPosition (finance)Tracking (education)Computer sciencePosition trackingControl (management)Computer visionArtificial intelligenceEngineeringMechanical engineeringPsychology

Abstract

fetched live from OpenAlex

This study proposes an effective control strategy for autonomous excavation under complex ground conditions, by integrating position, contour, and force control that are mutually associated factors. For the position control strategy, a non-linear PI controller was devised to control the stroke of each hydraulic cylinder, and therefore the bucket tip's position. To compensate for the ground resistive forces in contact space, an impedance controller was designed. Finally, contour compensation was considered to generate an optimal path of the bucket tip for ground leveling tasks. The performance of developed control algorithms was evaluated in the case of ground leveling task through co-simulation in multi-physics domains. Simulation results show that the designed control scheme provides good results in terms of transient response and tracking accuracy by dealing with all the aspects of force, position, and contour compensation.

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 score0.805
Threshold uncertainty score0.195

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 head0.021
GPT teacher head0.244
Teacher spread0.224 · 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
Published2020
Admission routes1
Has abstractyes

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