Sensorless Speed Control of SPMSM Using Disturbance Rejection Predictive Functional Control
Bibliographic record
Abstract
This work investigates the speed regulation issue for Permanent Magnet Synchronous Motors (PMSM), which is typically characterized by nonlinearity, uncertainty, and disturbances. A bridge between Porpotional Integral Derivative (PID) and complicated Model Predictive Control (MPC) is Predictive Function Control (PFC). Dead time and constraints are two things that PID control may struggle with, whereas PFC can overcome these challenges. PFC is a straightforward MPC that uses prediction and can be deployed using simple software and cheap hardware equipment. The PFC approach is incorporated into the control design of the speed loop to maximize the control performance of the PMSM. In this approach, a simplified model is used to predict the q-axis current of PMSM for the future step. Then, a quadratic performance cost index is minimized to produce an ideal control law. It should be noted that in the presence of significant disturbances, the conventional PFC approach does not yield satisfactory results. The load and other disorders are restrained by producing compensation current via feedforward compensation in real-time. The discrete Lyapunov function and Popov super stability theory confirm the stability of the suggested approach.
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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 | 0.000 |
| 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".