Advanced Control of Switched Reluctance Motors (SRMs): A Review on Current Regulation, Torque Control and Vibration Suppression
Bibliographic record
Abstract
With the increasing environmental concerns, a paradigm shift towards electric and hybrid electric vehicles is expected. Switched Reluctance Motors (SRMs) have emerged as a viable competitor to other established electrical machines. SRMs are known for their simple construction, robustness, inherent fault tolerant structure and low production and maintenance costs. Moreover, the machine has gained interest due to the absence of permanent magnets or windings in the rotor structure, which significantly reduces production costs when compared to other electric motors. The SRM, however, present some known drawbacks, such as increased torque ripple and acoustic noise production, as well as a highly nonlinear behavior. Through the use of adequate control strategies, however, the main challenges of the machine can be overcome. Thus, this paper presents a state-of-the-art review of the advanced control of SRMs, encompassing current regulation strategies, torque control strategies and vibration suppression techniques. First, two categories of current controllers are reviewed: model-independent and model-based. Next, indirect and direct torque control methods are explored. Then, three approaches to vibration suppression are discussed, namely active cancellation, current profiling and direct instantaneous force control. Lastly, a summary of each topic is presented and suggestions of future research topics are listed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".