An application of a finite element controller map for speed control for saturated induction motors
Bibliographic record
Abstract
A novel Finite Element Controller Map (FECM) method for the speed control of a saturated induction motor (IM) drive is presented in this paper. The newly developed algorithm is based on approximate functions in finite elements, which are expressed according to the nodal values of output response of the IM drive. The map complexity depends on the nodes assigned within a discrete element. The proposed FECM improves dynamic responses, and is designed by assigning a simple shape function to avoid computational burden. The complete controller scheme incorporating the FECM algorithm for an IM drive is developed in MATLAB/Simulink. The proposed FECM algorithm is experimentally implemented in real time using a DSP-DS1104 control board for a laboratory induction motor. The simulated performances of the proposed FECM are found to be not very sensitive to parameter variations. These simulation results are investigated and compared with a conventional PI controller, when they are subjected to changes to command speed and parameters variation, particularly at low speeds. The simulation and experimental results are provided to verify the effectiveness of the proposed control strategy.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".