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Robust Control of Drill String in Horizontal Boreholes of Coal Mines Considering Wall Friction

2025· article· W4415968882 on OpenAlexaff
Xiao Liu, Luefeng Chen, Chengda Lu, Min Wu, Witold Pedrycz

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesChina University of GeosciencesNational Natural Science Foundation of China
KeywordsDrill stringCoal miningBoreholeRobustness (evolution)WeightingControl theory (sociology)DrillRobust control

Abstract

fetched live from OpenAlex

In coal seam drilling, wall friction along the borehole introduces complex, spatially distributed disturbances that degrade the dynamic performance of the drill string. Due to its slender and flexible structure, the drill string is highly sensitive to such multi-point excitations, leading to frequent velocity fluctuations and reduced tracking accuracy. To address this, a robust H-infinity control strategy is proposed. A disturbance weighting function is designed to give the controller notch-filter characteristics, enabling targeted suppression of resonance-induced vibrations. Relying only on inlet measurements, the controller ensures accurate tracking of the reference feeding speed while effectively mitigating wall friction effects. Simulation results show significant improvements in steady-state accuracy, disturbance rejection, and robustness compared to conventional methods, confirming the effectiveness of the proposed approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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