Applications of Risk-Averse Control Policies on Permanent Magnet Synchronous Machine Drives
Bibliographic record
Abstract
Permanent magnet synchronous machines (PMSMs) are a family of electric motors that offers high power-density, large torque-to-inertia ratio and precise speed/position regulation. However, PMSMs have complicated nonlinear system dynamics and are subject to various sources of disturbances. In this research, we view the unpredictable disturbance sources as stochastic noises with known distribution functions. We also exploit the synchronous feature of PMSMs to linearize and discretize the mathematical model at the nominal speed. Two risk-averse stochastic control policies, linear-exponential-quadratic regulator (LEQG) and risk-constrained linear-quadratic regulator (LQR) are used to regulate the system. On the linear PMSM model, the control policies are able to reduce the cost variance at the expense of higher average cost. When applied to the nonlinear PMSM model, the control policies simultaneously lower the average cost and the variance for a certain range of risk-averse parameters. The findings provide valuable information about handling external disturbances during PMSM operation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".