Derivative-Free Direct Search Optimization Method for Enhancing Performance of Analytical Design Approach-Based Digital Controller for Switching Regulator
Bibliographic record
Abstract
Although an analytical design approach-based digital controller—which is essentially a deadbeat controller—shows zero steady-state error and no intersampling oscillations, it takes a finite number of sampling periods to settle down to a steady-state value. This paper describes the application of a derivative-free Nelder–Mead (N–M) simplex method to the digital controller for retuning of its coefficients intelligently to ensure improved settling and rise times without disturbing the deadbeat controller characteristics (i.e., no ripples between the sampling periods and no steady-state error). A switching-mode buck regulator working at 1 MHz in continuous conduction mode (CCM) is considered as a plant. Numerical simulation results depict that the N–M algorithm-based optimized digital controller not only shows improved steady-state and transient performance but also guarantees rigorous robustness against model uncertainty and disturbance as compared to its traditional counterpart, as well as the other optimized digital controller fine-tuned through other derivative-free metaheuristic optimization techniques, such as the genetic algorithm (GA). A system generator-based hardware software co-simulation is also performed to validate the simulation results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".