Inner-Loop Control for Electro-Hydraulic (EHA) Actuation Systems
Bibliographic record
Abstract
This article presents the development of a multiple inner-loop control strategy for improving the performance of hydrostatic actuation systems. In these actuators, the presence of non-linearities associated with pump/motor static friction and backlash, pressure drop in the piping system, and nonlinear friction at the load have a significant effect on the performance and positional accuracy of the system. The effect of nonlinear friction at the pump/motor interface has been overcome by the use of a high gain pump-speed inner-loop control strategy. In this article, the concept of inner-loop control will be extended to target specific sources of performance degradation. Velocity feedback will be incorporated in this manner to decrease the effects of pump backlash and nonlinear friction at the load. Simulation results supported by theoretical analysis indicate that a considerable improvement in performance can be achieved by the implementation of this control strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".