Dead-Time Compensation for Model-free Predictive Control in Multilevel Inverters
Bibliographic record
Abstract
Model predictive control (MPC) has been widely applied to various medium-voltage power converters due to its advantages in good dynamic performance and multi-objective control. However, the control algorithm requires the accurate model in the controlled system. As a result, the parameter mismatch between the actual model and control module leads to an undesired performance of MPC. Therefore, model-free predictive control (MFPC) has been developed in the literature to solve this issue. This approach uses a recursive least square (RLS) algorithm to estimate parameters in the model based on the measured values. In such systems, the dead-time makes the model nonlinear and leads to a poor prediction in the output currents and leads to the current distortion. In this paper, a dead-time compensation algorithm based on MFPC method is proposed to obtain a linear model and reduce the negative effects of dead-time. This can be done by compensating the phase voltage in the controller with the voltage second theorem. The dead-time compensation method is verified by simulation results.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".