Sensorless Self-Tuning Digital CPM Controller With Multiple Parameter Estimation and Thermal Stress Equalization
Bibliographic record
Abstract
This paper introduces a practical sensorless average current-programmed mode controller for low-power dc–dc converters operating at high switching frequencies. The controller accurately estimates inductor currents and identifies main converter parameters. Namely, total conduction losses in each of the phases as well as the inductors and output capacitance values are identified. The estimate of the losses is used to monitor temperature of the components without costly thermal sensors and for current sharing based on thermal stress equalization increasing system reliability. The identified filter values are utilized in a transient-mode controller for obtaining response with virtually minimum output voltage deviation. The key element of the new controller is a self-tuning digital multiparameter estimator that operates on the inductor time-constant matching principle. It estimates the average inductor current over one switching cycle using an adaptive IIR filter and, in the same process, identifies other converter parameters. The operation of the controller is verified with a single-phase 12 to 1.5 V, 15 W and a dual-phase 12 to 1.8 V, 80 W buck converter prototypes operating at 500 kHz switching frequency. The results show that the controller estimates the current and temperature of the components with better than 10% accuracy, effectively equalizes phase temperatures, and provides virtually minimum output voltage deviation during load transients. The implementation also shows that the controller is well suited for on-chip implementation. Its full realization requires less than 16 000 logic gates and two relatively simple ADCs that, in a standard 0.18-μm CMOS process, can be implemented on a small silicon area, no larger than 0.6 mm$^2$.
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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 | 0.001 | 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) | 0.002 | 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".