Nonlinear Programming Optimization Towards Optimal Transition Design in Model Free Predictive Control
Bibliographic record
Abstract
Model free predictive control (MFPC) is a promising control approach which not only obviates the need for electric machine model, but also it is substituted with the conventional PI current controller and the pulse width modulation (PWM) resulting in removal of integrator and PWM, consequently less control effort. Voltage/current slope lookup table-based MFPC method has attracted notable attention due to its merits in terms of robustness and low computational effort. Since online voltage/current lookup table data is required to drive the electric motor, this method is incapable of direct motor startup. This study presents an optimal transition design from conventional to voltage/current lookup table-based MFPC method. The optimal transition problem has been derived analytically at first as a nonlinear objective function with two linear constraints. Afterwards, a nonlinear programing optimization algorithm is employed to solve the problem and simulation results are provided to support the efficacy 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".