A Predictive Gradient-Based Filtering Method for State Estimation of MEMS Micromirrors
Bibliographic record
Abstract
In this article, a novel dynamic filtering method called predictive gradient based filtering (PGBF) scheme is proposed for state estimation of electromagnetic scanning micromirrors (ESMs) disturbed by noise. In this method, a random state space Hammerstein model with hysteresis is established to describe the characteristic of ESM with hysteresis in random noise environment. Then, the predictive gradient-based filter based on the constructed model is developed to suppress the influence of random noise on such nonlinear systems. To cope with the impact of model uncertainty, a model error compensation mechanism (MECM) is introduced into the PGBF algorithm. Due to the predictive gradient-based optimization (PGBO) method being able to predict the gradient direction of the system cost function within certain horizon in the future, it enables the filter to make decision to avoid being trapped in some local extrema and obtain satisfactory filtering results. Then, the convergence of PGBF is analyzed. Subsequently, the proposed filtering method is applied to the state estimation of ESM disturbed by noise. The proposed filtering scheme is compared with the unscented Kalman filtering (UKF) and nonsmooth Kalman filtering (NKF) strategies. The experimental results show that the proposed PGBF method can achieve better performance in both accuracy and convergence speed of state estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".