Advanced Control of Switched Reluctance Motors (SRMs): A Review on Current Regulation, Torque Control and Vibration Suppression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it