An Adaptive Nonlinear Current Observer for Boost PFC AC/DC Converters
Bibliographic record
Abstract
Power factor correction (PFC) is an essential part of ac/dc converters in order to improve the quality of the current drawn from the utility grid. The PFC closed-loop control system requires a precise measurement of the boost inductor current in order to tightly shape the input current. Current sensors are widely used in the PFC closed-loop control system to measure the boost inductor current. Current sensors introduce delay and noise to the control circuitry. Also, they significantly contribute to the overall cost of the converter. Therefore, they make the implementation of the PFC converter complicated and costly. Current sensorless control techniques can offer a cost-effective solution for various applications. The unique structure of the boost PFC converter makes it challenging to robustly estimate the inductor current due to the nonlinear structure of the converter. Also, it is shown in this paper that the system loses observability at some singular operating points, which makes the observer design more challenging. In addition, the load value is unknown in most applications. Thus, the observer should be able to estimate the inductor current in the presence of uncertainties in the load and other parameters. This paper presents an adaptive nonlinear observer for the boost PFC, which is able to accurately estimate the inductor current. The adaptive structure of the converter allows the robust and reliable performance of the observer in the presence of parameter uncertainties, particularly load variations. Also, an auxiliary compensation is integrated into the observer to circumvent the singular operating points and provide a precise estimation for the entire range of operation. Experimental results are presented to verify the feasibility of the proposed sensorless control approach.
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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 | 0.000 |
| 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".