Accurate Position Control for Hydraulic Servomechanisms
Bibliographic record
Abstract
Accurate Position Control for Hydraulic Servomechanisms Manuel Pencelli, Renzo Villa, Alfredo Argiolas, Gianni Ferretti, Marta Niccolini, Matteo Ragaglia, Paolo Rocco and Andrea Maria Zanchettin Pages 250-257 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: A common problem when operating heavy hydraulic machines consists in the inability of performing accurate motion. In the last few years the development of non-linear control techniques and the production of increasingly more accurate and cheaper hydraulic drives induced a steadily growing interest towards the development of controlled hydraulic systems. Clearly, this interest is also motivated by the possibility to exploit the huge power density, which is an intrinsic feature of hydraulic systems. This paper specifically focuses on the development of several position control schemes, whose aim is to guarantee accurate motion of a standalone hydraulic servomechanism. By relying on an experimentally validated mathematical model of the servomechanism itself, different control schemes have been synthesized and the resulting control performances have been verified and compared together. Keywords: Hydraulic systems, Hydraulic actuators, Motion Control DOI: https://doi.org/10.22260/ISARC2019/0034 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".