A Systematic Approach for PLL-Based Zeta Power Converter Control
Bibliographic record
Abstract
This paper presents a systematic approach for Zeta power converter control, a versatile solution designed for systems where the power supply is prone to fluctuations. The optimized controller exploits a detailed model of the power stage and adjusts the system parameters to ensure stability over a wide range of loads and loading conditions. A detailed model for the Zeta converter is developed, considering important power stage parameters such as the ON resistance of power transistors and inductor series resistance. The control strategy utilizes a phase-locked loop, which includes a loop filter along with an additional lead compensator circuit to improve the loop phase margin, thereby enhancing the system’s dynamic response and stability. The proposed graphical approach facilitates intuitive controller design and tuning, providing a robust framework for managing converter dynamics. The system stability and performance are validated through extensive transient simulations using a standard 180 nm CMOS technology, demonstrating the converter’s effectiveness in maintaining stable output under variable input conditions. Experimental results show that the proposed closed-loop Zeta converter can achieve a peak efficiency of 94% when the load resistance is$10~\Omega $, and it can handle current loads up to 3A. The system operates at a switching frequency of 85 kHz and can support an input voltage range from 6V to 34V while maintaining stable output. During reference tracking tests, the system demonstrates excellent transient response, with a settling time of 12.5 ms and a peak overshoot of 3.9V. Additionally, compared to similar works, the system exhibits superior normalized transient load regulation, highlighting the robustness of the proposed control strategy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".