Multiparameter Estimation of DC-DC Boost Converter via TA-PINN Without High-Frequency Measurements
Bibliographic record
Abstract
Accurate parameter estimation for DC-DC boost converters is essential for enabling their health monitoring, fault diagnosis and optimal control. However, under regular sampling configurations, DC-DC boost converters experience uncertain voltage transients at switching instants due to inherent sampling delays. The absence of such critical transient information makes high-precision multi-parameter estimation of boost converter heavily dependent on additional high-frequency measurement circuits. To solve this challenge, this paper proposes a Transient-Aware Physics-informed neural network (TA-PINN). By enhancing the conventional PINN framework with physics-guided, weakly-supervised mechanism, TA-PINN constrains the embedded system model to conform to both the sampled system dynamics and the physical laws governing the converter, including its discontinuous output voltage transients. As a result, TA-PINN alleviates the high reliance for additional high-speed measurement circuitry typically required by existing methods, and can achieve accurate parameter estimation using data sampling configurations compatible with commonly used microcontroller units (MCUs). The experimental results verify the performance of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".