Multi-rate Finite Control Set Model Predictive Control with Reduced Circulating Currents for Parallel Dual-Converter-Fed PMSM Drive
Bibliographic record
Abstract
In the field of electrical drives, finite control set model predictive control (FCS-MPC) scheme is widely used due to its fast-dynamic response and superior ability to handle nonlinear constraints. However, the typical practical switching frequency of FCS-MPC is relatively lower than its sampling frequency, which limits the improvement of power density and efficiency. This problem becomes more pronounced when implementing FCS-MPC to parallel-connected converters, where the heavy calculation burden imposes limitations on employing high sampling frequency. As a result, large circulating currents are inevitable. To effectively suppress the circulating current, an improved multi-rate FCS-MPC scheme is proposed, where the reduction of circulating current is realized with two aspects. First, the control set is modified by categorizing the control variables into two groups, hence the regulation of circulating current is decoupled from the motor-side variables, resulting in improved precision and effectiveness of circulating current control. In addition, exhaustive enumeration of all possible switching states is also avoided. Second, multi-rate technique is integrated, where multiple control cycles are inserted within one sampling interval. Experimental results verify the effectiveness of the proposed scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".