A Predictive Gradient-Based Observer for Fault Detection of MEMS Micromirrors
Bibliographic record
Abstract
This paper proposes a predictive-gradient-based Extended State Observer with Hysteresis (ESOH) for online fault detection of Electromagnetic Scanning Micromirrors (EMSM)—a key MEMS optical actuator—addressing the critical challenge of rate-dependent hysteresis that degrades traditional observer performance. In this approach, a State-Space Model incorporating Rate-Dependent Hysteresis (SSMRDH) is developed to depict the properties of EMSM. Then, according to the constructed model and considering the suppression of model errors and unknown disturbances, the ESOH is developed for the state estimation of EMSM. To obtain the optimal ESOH and taking into account the EMSM, a Predictive Gradient-Based Learning (PGBL) algorithm is applied to the online determination of the observer’s gain vector, thus preventing the learning search process from being trapped in the local extrema resulting from the rate-dependent hysteresis in EMSM. Thereafter, the convergence analysis of the online-learning ESOH is investigated. Subsequently, the online predictive-gradient-based ESOH is utilized for the online fault detection of EMSM. In order to cover the entire cycle from the occurrence to the development of a fault, improve the fault detection rate and reduce the fault detection delay, a fusion decision threshold method that integrates information entropy and relative divergence is proposed. At the end of the paper, the experimental results are presented to verify the proposed fault-detection strategy.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 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".