Application of Finite Control Set–Model Predictive Control for Servo Brake Motion in PMSM Drives
Bibliographic record
Abstract
Finite control set-model predictive control (FCS–MPC) has a simple and intuitive optimization procedure. Therefore, FCS–MPC is increasingly being applied to control strategies for electrical drive systems. This article presents a method for servo brake control of a permanent magnet synchronous motor (PMSM) based on FCS–MPC. Accordingly, we propose a reference trajectory introduced in a cost function for brake motions and an alternating procedure with speed control. Moreover, this article clarifies the problem peculiar to servo-brake control using FCS–MPC, i.e., the reduction in tracking performance near the brake position because of the low resolution of the output voltage. In addition to the conventional method, a finite number of smoothed voltages were applied as candidate voltages for FCS–MPC to improve the tracking performance near the brake position. The smoothed voltages can effectively increase the resolution of the output voltage, which results in fewer steady-state errors in angular position tracking during servo brake motion. The simulation and experimental results obtained using a PMSM drive system reveal that the proposed strategy exhibited high performance in tracking the reference target during the operation of servobrakes and the ability to seamlessly alternate between servo brake and motor operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".