Flux Estimation and Control Based On High-Gain Observer for Variable Reluctance Actuator Using the Measured Current Only
Bibliographic record
Abstract
Measuring magnetic flux plays a crucial role in developing a linearized controller for a variable reluctance actuator. However, the method used for measuring magnetic flux may have limitations that render it invalid under different operating conditions. This paper presents a flux estimation and control method based on the Extended High-Gain Observer (EHGO) approach for reluctance actuators, utilizing only the measured current. Initially, the dynamic model of the reluctance actuator is formulated in terms of the current in the coil, considering the magnetic field as unknown. Subsequently, EHGO is designed to use the measured current to estimate the magnetic field and magnetic flux. Finally, a feedforward controller is introduced to utilize the estimated magnetic field for achieving tracking performance of the desired magnetic flux. The analysis of the error bound in tracking errors demonstrates that minimizing the estimation error of the magnetic field reduces the tracking error. The effectiveness of the proposed flux estimator and control approach is evaluated through numerical simulations. The results illustrate the EHGO's capability to achieve accurate magnetic flux estimation, improving the tracking performance of the feedforward controller and minimizing tracking errors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".