A Novel Adaptive Force Ripple Suppressing Method for Double-Sided Switched Reluctance Linear Motor
Bibliographic record
Abstract
This article proposes a method to suppress electromagnetic force ripple in switched reluctance linear motors (SRLMs). While the force distribution function (FDF) method is effective, conventional approaches employing fixed functions limit adaptability to varying conditions, and existing adaptive algorithms often exhibit suboptimal online adjustment. To overcome these limitations, this study introduces an adaptive reference trajectory that dynamically adjusts based on mover speed and load. In addition, turn-on/off positions are adaptively modified according to a commutation point selected by the force-per-ampere rate; the selection of the commutation point reduces the current root-mean-square (RMS) and significantly enhances the tracking performance of the system. By transforming the Sigmoid function, the reference trajectory is defined by only two parameters, enabling easy online integration of the adaptive function. Furthermore, a multistep continuous control set model predictive controller (CCS-MPC) with self-correction is adopted as the current controller to improve tracking performance. Simulation and experimental results demonstrate superior regulation performance compared to genetic algorithm (GA) iterative methods and the conventional FDF approach. Compared with the GA algorithm, this method reduces the electromagnetic force ripple by an average of 11.3% and the average current RMS by 2.7%.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".