An Enhanced Active Disturbance Rejection Control of PMSM Based on ILC and Parameter Self-tuning
Bibliographic record
Abstract
Conventional model-based permanent magnet synchronous motor (PMSM) drivers suffer deteriorated dynamic performance from the inward and outward disturbance. A new control method is proposed to improve the robustness of PMSM drivers in transient-state operation in this paper. Ant colony optimization (ACO) is utilized to tune parameters of active disturbance rejection control (ADRC). By using ACO's self-learning ability and multiple iterative calculations, the optimal solution can be quickly calculated, thereby reducing the difficulty of ADRC parameter adjustment. Besides, the torque ripple changes periodically with the rotor position and causes speed fluctuations, which reduces the PMSM system's dynamic performance. Usually, the PI controller and iterative learning control (ILC) in parallel are used to suppress torque fluctuations. However, it is very sensitive to the system uncertainty and external interference, that is, it will be paralyzed by non-periodic interference. Therefore, the ILC-ADRC is proposed in this paper to both reduce the ripple and guarantee robustness. The simulation results demonstrate the superior robustness of the proposed ADRC to that of the traditional method in transientstate and steady-state 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".