Dynamic‐surface‐based adaptive predefined‐time control for nonlinear non‐affine switched systems with sensor fault
Bibliographic record
Abstract
Abstract The adaptive neural tracking fault‐tolerant control problem is considered for nonlinear non‐affine switched systems with sensor faults via dynamic surface control (DSC) technique under arbitrary switchings within predefined‐time interval. During the controller design process, the non‐affine formation strictly processed so that the implicit control signals can be transformed into explicit ones. The introduction of hyperbolic tangent function to design the control signal eliminates the singularity at the same time, but also avoids the tedious discussion of the segmentation function to solve the singularity. Considering Lyapunov stability theorem, an adaptive fault tolerant control approach is presented, which means that the settling‐time can be programmed by the user practical specification under arbitrary switching, the predefined time boundedness of all closed‐loop signals can be ensured, and the influence of sensor faults can be compensated. The effectiveness of the presented method is verified via simulation results.
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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 | 0.001 | 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 | 0.000 |
| 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 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".