A Fast Nonsingleton Type-3 Fuzzy Predictive Controller for Nonholonomic Robots Under Sensor and Actuator Faults and Measurement Errors
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Bibliographic record
Abstract
This study proposes a novel control scheme for simultaneously tracking and stabilizing nonholonomic wheeled mobile robots (NWMRs) subject to actuator and sensor faults, measurement errors, uncertain dynamics, and time-varying slippage/skid disturbances. To this end, a nonlinear model based on a type-3 (T3) fuzzy logic system (FLS) is developed for NWMR tracking and stabilization. Furthermore, a nonlinear model predictive controller (NMPC) is designed analytically without employing iterative computations, thus achieving fast performance. A new approach of type-3 nonsingleton fuzzification is introduced to handle measurement errors. Additionally, faults in the actuators and sensors are detected by a supervisory scheme and eliminated by a devised compensator. Finally, extensive simulations and experimental validations are conducted to further verify the effectiveness of the proposed scheme, along with a comparative analysis of several benchmarking methods.
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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) | 0.000 | 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) | 0.000 | 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 it