An Active Fault-Tolerant Control Strategy for Current Sensors Failure for Induction Motor Drives Using a Single Observer for Currents Estimation and Axes Transformation
Bibliographic record
Abstract
This paper proposes a fault-tolerant control technique against current sensors failure in direct torque controlled induction motors drives, based on a new modification of Luenberger observer for currents estimation and axes transformation for vector rotation. Several important aspects are covered in the proposed algorithm, such as the detection of sensors failure, the isolation of faulty sensors, and the reconfiguration of the control system by a correct estimation. A logic circuit ensures fault detection by analyzing the residual signal between the measured and estimated quantities, while a single observer performs the task of estimating the line currents. In addition, a decision logic circuit isolates the erroneous signal and simultaneously selects the appropriate estimated current signal. An axes transformation ensures rotation from (a,b) to (α,β), which keeps a low-cost control using only two current sensors. The proposed scheme is tested on MATLAB/Simulink environment and experimentally validated in a laboratory prototype mainly containing a dS1104 card and 4 kW induction motor.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".