A Resonant Controller With High Structural Robustness for Fixed-Point Digital Implementations
Bibliographic record
Abstract
This paper presents a new resonant (R) controller. The proposed R controller is input-output equivalent to the conventional R controller but it is internally nonlinear. Its internal state variables are the transformed versions of the conventional R controller into the polar coordinates. It is, thus, given the name of polar form resonant (PFR) controller. While the PFR is totally equivalent to the R controller in continuous-time domain, it offers a much higher structural robustness when it comes to digital implementations. Particularly, it is shown in this paper that the PFR resolves the well-known structural sensitivity of the R controller for applications that need high sampling frequency and have word length limitations. Such a structural sensitivity is conventionally resolved by resorting to the delta-domain realizations. The PFR offers an alternative method to the delta-domain realization technique with even higher degree of robustness and easier stage of adjustment. Moreover, the PFR can easily be enhanced to accommodate frequency variations, a feature that is not easily attainable using the delta-domain method. Feasibility of the PFR controller is verified using a laboratory prototype of a single-phase uninterruptible power supply system operating at high sampling and switching frequencies where the control system is implemented on a field programmable gate array board.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".