Current Injection-Based Multi-parameter Estimation for Dual Three-Phase IPMSM Considering VSI Nonlinearity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To develop a high-performance and reliable control for dual three-phase interior permanent magnet synchronous motor (IPMSM), accurate knowledge of machine parameters is of significance. This paper proposes an improved recursive least square (RLS) algorithm and a current injection-based parameter estimation method for dual three-phase PMSM with consideration of inverter nonlinearity and magnetic saturation. First, the vector space decomposition (VSD)-based dual three-phase PMSM model is established. The inverter nonlinearity model for dual three-phase PMSM is derived, and the cross saturation and the self-saturation of DQ1-axis inductances are modeled to improve the estimation accuracy. Finally, the machine parameters, including winding resistance, rotor flux linkage, and varying DQ1-axis inductances under different operating conditions, are estimated using the proposed current injection-based method with the RLS algorithm. Compared with existing methods, the proposed approach can achieve better estimation performance and is validated on a laboratory dual three-phase IPMSM under different temperature and operating conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it