A new tracking error-based adaptive controller for servo-hydraulic actuator control
Bibliographic record
Abstract
Real-time hybrid simulation (RTHS) is a testing method which combines the response from an experiment (i.e., experimental substructure) with that of a computer model (i.e., analytical substructure) in real-time. The accuracy and stability of the RTHS are prone to the propagation of error in the measured signals. Thus, critical developments in servo-hydraulic actuator control are needed to enable a wide application of this testing technique. In this study, a new tracking error compensation strategy for servo-hydraulic actuator control is developed, and numerically and experimentally evaluated. This compensation procedure is formulated based on a new set of tracking error indicators, namely, frequency domain-based (FDB) error indicators, which uncouple phase (lag and lead) and amplitude (overshoot and undershoot) errors and quantify them. These indicators are then incorporated into a two degree-of-freedom (d.f.) controller to develop closed-form equations to design an adaptive servo-hydraulic controller with improved tracking performance. The FDB indicators and the new adaptive controller are studied through numerical simulations in Simulink and LabVIEW and also verified experimentally. The proposed controller is computationally efficient, it can be implemented in real-time and it does not require any user-defined (i.e., predetermined) coefficients. As a result of its two d.f. formulation, this adaptive controller can be introduced to any closed-loop servo-controller through a digital or analog interface depending on the experimental setup properties. As such, it can be used to improve the tracking capability of any single hydraulic actuator system, which is essential in RTHS.
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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 | 0.001 | 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".