Robust H-Infinity Control of Feeding Speed in Coal Seam Drilling Process With Uncertain Hardness
Bibliographic record
Abstract
The hardness of the coal seam significantly influences the relationship between feeding speed and resistance at the drill bit. Due to the compressive deformation of the drill string, maintaining a stable feeding speed during drilling remains challenging. In this article, we propose a robust H-infinity dynamic output feedback controller, formulated to explicitly address uncertainties in formation hardness, thereby ensuring stable feeding speed and improved dynamic performance of the feed system under diverse drilling conditions. First, we develop a bitrock interaction model that incorporates the uncertainties in formation hardness and the rock-breaking threshold, which are key factors affecting drilling performance. By integrating this model with a finite element representation of the drill string, the feeding system is recast as a norm-bounded uncertain system. On-site data is utilized to replicate drilling conditions and validate the accuracy of the model. Subsequently, considering the uncertain parameters and industrial performance requirements, a dynamic output feedback controller is designed using robust H-infinity optimization with tailored weighting functions. This controller maintains stable feeding speed across varying formation hardness, effectively suppressing the impact of hardness fluctuations by attenuating resonance peaks. Both simulation and field experiments confirm that the proposed controller substantially reduces feeding speed fluctuations and achieves the desired robustness and control performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".