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 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".