Sensor Fault Reconstruction and Active Fault Tolerant Control Based on PF&RBF Optimization
Bibliographic record
Abstract
The failure of the braking system pressure sensor will significantly affect vehicle safety. A new fault reconstruction method based on Particle Filtering (PF) & Radial Basis Function (RBF) neural network and an active fault-tolerant control strategy based on Non-Singular Global Fast Terminal Sliding Mode Control (NSGF-TSMC) are proposed for the pressure sensor fault. PF reduces the noise of the output signals of the actual system. Nonlinear characteristics - such as the time delay of the braking system - are identified by RBF, following which the nonlinear dynamic model of the system is constructed. Second, by using theoretical derivation, the analytical solution of the fault is reconstructed, and the real-time reconstruction algorithm of the sensor fault is proposed. Third, based on fault reconstruction and the nonlinear model of the system, a new active fault-tolerant control algorithm is designed, utilizing an NSGF-TSMC, which ensures the stability of the system at the cost of only a slight performance loss under fault conditions. Finally, considering the safety factors, the algorithm is verified in HIL, with results showing that the proposed method can ensure the stability of the system and retain good fault-tolerant control capabilities under various sensor fault conditions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".