Adaptive finite‐time fault‐tolerant control for flexible‐joint robotic stochastic systems with input saturation and sensor fault
Why this work is in the frame
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Bibliographic record
Abstract
Summary A new fault‐tolerant approach to input saturation and sensor faults is presented for flexible‐joint robotic stochastic nonlinear systems with finite‐time convergence performance. More precisely, a new command filter is designed and embedded into the backstepping framework, which greatly reduces the amount of calculation. By using the estimation ability of fuzzy logic system and less adjustable parameters, the problems of sensor faults and unknown functions are solved, which facilitates the design of controllers. A smooth function and mean‐value theorem are used to deal with the difficulty associated with system signals. A novel simpler controller is developed to ensure that the trajectory tracking error converges to a sufficiently small neighborhood around the origin within finite time, while there are random noises. Finally, the effectiveness of the presented scheme is verified by simulation results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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