MPC-Based Coordination Control of Dual Direct-Drive Permanent Magnet Motors Used in Coal Mining Belt Conveyors
Bibliographic record
Abstract
In the application of coal mining belt conveyors, dual-motor drives based on permanent magnet motors (PMM) are gaining increasing attention now. To achieve high-performance coordination control of the two motors, this paper proposes a finite control set model predictive speed control (FCS-MPSC) method to improve the dynamics and speed tracking performance of the motors. First, the features of the dual-motor drives used in conveyor belts are analyzed. On this basis, the requirements of the control strategies are illustrated. Second, a master-slave control strategy is developed after treating the PMMs at the tail end and head end as the master motor and slave motor, respectively. Third, the FCS-MPSC method is developed for both master and slave motors by using new predicting model. In this process, the issue that the speed property is not directly related to the manipulated variables are tackled. Moreover, in order to further improve the dynamics of the slave motor, a speed reference compensation strategy is proposed. Finally, the proposed FCS-MPSC method is validated through comparative simulation results.
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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 | 0.000 |
| 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 | 0.001 | 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".