Improved dynamic and steady state performance of a hybrid speed controller based IPMSM drive
Bibliographic record
Abstract
This paper presents a high performance interior permanent magnet synchronous motor (IPMSM) drive system based on a hybrid intelligent speed controller. Closed loop vector control technique is applied to model the drive system and the hybrid speed controller is designed as a combination of a PI controller with fuzzy inference system. The speed controller is designed in such a way that satisfactory speed and torque responses can be attained in both steady state and dynamic conditions. A flux controller is also incorporated so that both torque and flux of the motor can be controlled while maintaining current and voltage constraints. Thus the proposed drive widens the operating speed limits for the motor and enables the use of the reluctance torque. To investigate the performances in both transient and steady state conditions, the results of the proposed IPMSM drive system are compared with those of the conventional PI controller based drive in simulation. The proposed IPMSM drive is also implemented in real-time using DSP board DS1104 for a laboratory 5 HP motor. Both simulation and experimental results demonstrate the better responses in terms of torque and speed for the proposed drive over a wide speed range.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".