Design of a low-bandwidth position controller based on system identification for an electro-hydrostatic actuator
Bibliographic record
Abstract
In order to design a controller, mathematical model is usually derived first, either from physical laws or by employing a system identification technique. Physical laws may not fully define the system because of the existing uncertainties and/or difficulty to accurately model certain phenomenon. Therefore, the resulting controller may be too conservative. In this article, we design a low-bandwidth controller for an electro-hydrostatic actuator positioning system based on a system identification technique. The designed controller is also linear, fixed-gain and robust to system uncertainties. A set of offline parametric linear identifications are performed under different conditions, including various environmental stiffnesses, levels of actuator internal leakage, viscous dampings and load masses. The obtained family of identified models is then used to design a quantitative feedback theory controller that satisfies given tracking and stability specifications. In addition, the performance of the controller is examined against another quantitative feedback theory controller that is designed for the same system using physical laws. The performances of two controllers are examined on a test rig. Experimental results show that both quantitative feedback theory controllers are capable of maintaining actuator position within acceptable response envelope. However, the controller designed based on physical laws has higher bandwidth and therefore is more conservative.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".