Model Predictive Current Control Combined Sliding Mode Control for Flux Switch Permanent Magnet Machine Drive System
Bibliographic record
Abstract
A flux-switching permanent-magnet synchronous machine (FSPMSM) has shown advantages, including strong mechanical robustness, high torque density, and acceptable fault redundancy potential, and started to find a market in various fields in electric vehicle, ship, airplane, and wind generation. However, a double salient structure and a high number of pole pairs cause the FSPMSM to experience great torque ripple and converter switching reduction, compromising its performance. Optimizing the machine design can significantly decrease the speed ripple and torque, often resulting in increased manufacturing costs, lower efficiency, and lower power density. Alternatively, several control-based solutions have been explored. One of the existing methods to minimize the torque control ripple is model predictive control (MPC); the most attractive method among them is model predictive current control (MPCC). In the speed outer loop design of MPC, the traditional PI control approach is often employed in FSPMSM controller design due to its ease of use and stability. However, it is hard to obtain suitable results due to its low control accuracy. In order to address this issue, this paper suggests MPCC combined sliding mode control (SMC) for three phases of flux-switching permanent magnet motor to improve the dynamic response of the MPCC. The simulated results imply that the suggested SMC combined MPCC scheme presents acceptable dynamic performances compared to the conventional MPCC strategy.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".