Nonlinear Controller Based High Speed Control of IPMSM
Bibliographic record
Abstract
This paper proposes an adaptive backstepping based nonlinear controller for high speed control of an interior permanent-magnet synchronous motor (IPMSM) particularly, above the rated speed. This controller incorporates both torque and flux controls with parameter estimation adaptively. The nonlinear backstepping controller is introduced in order to introduce high performance speed tracking. The controller with constant parameters will lead to an unsatisfactory prediction of the performance because of nonlinear operation of interior permanent magnet synchronous motor owing to the magnetic saturation of these machines particularly at high speed conditions. In this work, an adaptive backstepping based control technique, considering system parameter variation, has been developed for an IPMSM. The proposed adaptive backstepping based high speed control of IPMSM drive is successfully implemented in real time for a laboratory 1 hp IPMSM drive. The efficacy of the proposed drive is verified both in simulation and experiment at different operating conditions. Form the results it can be concluded that the proposed controller is robust to dynamic uncertainties and can reject any bounded immeasurable disturbances entering the system. The voltage level control inputs are designated to follow the command speed using backstepping technology.
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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".