Computationally Efficient Multirate Continuous Control Set Model Predictive Control With Fast Response for PMSM Drives
Bibliographic record
Abstract
Multirate continuous control set model predictive control (CCS-MPC) facilitates higher switching frequencies by simultaneously optimizing control actions across multiple sub-cycles. However, conventional schemes do not incorporate voltage constraints during the optimization process. Consequently, when the commanded voltage exceeds the converter's output capability, output scaling becomes necessary. This not only compromises the performance of the current sub-cycle but also degrades the tracking accuracy of subsequent sub-cycles, inevitably impairing the system's dynamic performance. Moreover, high-dimensional matrix computations inherent to these schemes present substantial challenges for real-time implementation on cost-effective digital controllers. To overcome these limitations, this paper proposes a computationally efficient multirate CCS-MPC scheme with integrated overmodulation capability. The entire tracking process of these sub-cycles is strategically divided into three stages–chasing, transition, and maintaining–each targeting specific control objectives. By doing so, the original large-scale optimization is decomposed into three smaller subproblems, significantly reducing computational complexity. Specifically, the chasing stage incorporates dynamic overmodulation designed based on the shortest feasible path of flux linkage tracking to ensure rapid dynamic response, while in the maintaining stage, the rotor-movement effect across sub-cycles is accounted for, enhancing steady-state performance. Ultimately, experimental results validate the effectiveness of the proposed approach, demonstrating a fivefold improvement in dynamic response and a 62.8% reduction in total execution time.
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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) | 0.001 | 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 | 0.001 |
| 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".