Advanced model predictive control and power conversion strategies for flux-switching permanent magnet synchronous machines
Bibliographic record
Abstract
Modern electric drive systems, particularly in electric vehicles (EVs), renewable energy, aerospace, and industrial automation, demand efficient power conversion and robust motor control. Flux Switching Permanent Magnet Synchronous Machines (FSPMSMs) are gaining attention due to their high torque density, enhanced thermal performance, and durable structural design. However, their nonlinear behavior, parameter fluctuations, and susceptibility to external disturbances present significant control challenges. Conventional approaches like Field Oriented Control (FOC) and PI controllers often fall short in maintaining optimal performance under dynamic conditions. To address these limitations, Model Predictive Current Control (MPCC) has emerged as a viable solution, offering improved dynamic response, reduced torque ripple, and better current regulation. Despite its advantages, MPCC's reliance on accurate system modeling makes it prone to uncertainties. This research introduces a novel integration of Sliding Mode Control (SMC) into the speed loop, enhancing the system's ability to reject disturbances and adapt to varying conditions. The proposed MPCC SMC strategy demonstrates faster transient response, increased stability, and greater reliability, making it well suited for demanding FSPMSM applications. The approach is validated through high fidelity simulations using OPAL RT Technologies’ OP5707XG simulator. In addition to advanced motor control , a stable high voltage DC supply is crucial for efficient FSPMSM operation. Many energy sources, such as batteries, fuel cells, and photovoltaic (PV) panels, produce low voltage DC power, requiring an efficient step up converter for high performance motor drives. Traditional boost converters face challenges like extreme duty cycles, high conduction losses, and reduced efficiency, limiting their suitability. To address these issues, this research explores the Cubic Semi SEPIC Converter (C³SSC), a novel high gain, non isolated DC DC topology capable of achieving ultra high voltage conversion with moderate duty cycles, reduced switching losses, and improved efficiency. A laboratory tested prototype of the C³SSC confirms its high gain capability and practical viability for power conversion applications. While MPCC SMC ensures robust control of the FSPMSM, the C³SSC efficiently provides the necessary high voltage DC supply, enabling stable, efficient, and reliable motor operation. This research integrates advance d motor control with high performance power conversion, enabling next generation electric drives for sustainable transport, automation, and renewable energy.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".