Optimization-Driven Controller Design for a High-Performance Electro-Hydrostatic Asymmetric Actuator Operating in All Quadrants
Bibliographic record
Abstract
Abstract This paper presents an optimization-driven controller design for smooth and accurate position control of a single-rod electrohydrostatic actuator. The design approach uses logically guided iterative runs of the electrohydrostatic actuator to determine the optimal gain and poles' locations of a low-bandwidth controller. The optimization algorithm used in the paper is the globalized bounded Nelder–Mead algorithm with deterministic restarts for improved globalization and lower numerical cost. The design also incorporates a prefilter to ensure minimum jerk in the system's step input response in the beginning and while approaching steady-state. The step response of the filter is a seventh-deg polynomial curve that ensures the minimum change in acceleration in both states. Experimental results reveal that the addition of the proposed prefilter reduces jerk in the system by up to 90%. Results also indicate that the controller performs very well in all quadrants with external load uncertainty of up to 367 kg and thus proves the effectiveness of the design approach.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".