Local Dynamic Estimation-Based Robust Model-Free Predictive Control for PMSM Drives
Bibliographic record
Abstract
Model-free predictive control (MFPC) strategies have shown significant potential for achieving robust and highperformance operation in permanent magnet synchronous motor (PMSM) drives without relying on detailed system models. This paper presents a novel MFPC approach, termed the Local Dynamic Estimation Predictive Controller (LDEPC), which integrates a flexible real-time estimator with a refined local dynamics framework to capture unknown system behaviors. Two variants are proposed: the Zero-Order LDEPC, optimized for simplicity and ease of implementation, and the First-Order LDEPC, designed to improve disturbance rejection and capture nonlinear effects more accurately. The framework balances robustness, prediction accuracy, and computational efficiency, enabling effective control under parameter variations, rapid reference changes, and external disturbances. The effectiveness of LDEPC is experimentally validated on a PMSM test bench across multiple scenarios, including step responses, regenerative braking, parameter mismatch, disturbance rejection, and dynamic reference tracking. Comparisons with PI control, an MFPC using an ultra-local model and extended state observer (MFPCC-ESO), and an observer-enhanced MFPC with a generalized proportional-integral observer (MFPCC-GPIO) show that LDEPC consistently delivers robust, reliable, and precise control, underscoring its strong potential for practical deployment in demanding applications.
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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 | 0.001 |
| 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".