Fault Tolerant Neural Aided Controller for Multi Degree of Freedom Structures Experiencing Online Sensor Failure
Bibliographic record
Abstract
This study validates an adaptive control algorithm capable of compensating for online sensor failure. Online failure is a relevant problem when considering actively damped, multi-story smart buildings experiencing a disturbance event. In recent years, Artificial Neural Networks (ANNs) have proven very efficient in pattern classification and control applications. In this study, the unique application of ANNs involving Radial Basis Functions (RBFs) combined with H∞ optimal control has demonstrated three significant characteristic advantages: (1) real time adaptability, (2) optimal convergence and computation time, and (3) most importantly, no offline training. The novelty of the proposed controller is elucidated by performing disturbance rejection tests involving a scaled two degree of freedom shear frame subjected to a combined H∞ and ANN control. A bench scale structural model is instrumented with piezoelectric sensors and actuators. After the onset of a first mode disturbance, the structural frame is subjected to a complete sensor failure. The proposed controller is shown to enhance the performance of a baseline H∞ controller in the presence of sensor failure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".