Optimal and Fault-Tolerant Torque Control of Servo Motors Subject to Voltage and Current Limits
Bibliographic record
Abstract
This brief presents an optimal and ripple-free torque control of a brushless servo motor with any back-emk waveform that minimizes power dissipation subject to voltage and current limits of the motor's drivers/amplifiers. When one or more phases reach the voltage and/or current limits, the controller optimally reshapes the stator currents of the remaining phases for continuing accurate torque production. This allows the motor to operate above the rated speed and torque that would be achieved without current reshaping. In the event that an open-circuit or short-circuit of a winding occurs, the torque controller can also isolate the faulty phase in order to generate torque as requested given the voltage and current constraints of the healthy phases. Assuming the inductance of stator coils is negligible allows the description of the phase voltage and current limit requirements by a set of inequity constraints. It follows by the derivation of a closed-form solution for the optimal phase currents at given angular position, velocity, and desired torque—rendering the control algorithm suitable for real-time implementation. Experimental results illustrate the capability of the controller to achieve precise torque production during voltage/current saturation of the motor's drivers or a phase failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".