Online Estimation and Identification of Shape Memory Alloy-Actuated Flexible Structures Through Unscented Kalman Filtering
Bibliographic record
Abstract
This article tackles the problem of online state estimation and parameter identification for SMA-actuated flexible structures intended to be precisely controlled within a multivariable adaptive model-based framework. Using a joint state-parameter formulation, a non-linear recursive scheme has been developed that is capable of simultaneously producing online estimates of the states and the uncertain parameters from the noisy measurements at hand. The scheme employs an embedded model derived from reduced-order finite element modeling of the structure and phenomenological SMA modeling to incorporate the whole available knowledge about the nature of the system (non-linearity and hysteresis) and its uncertainties (uncertain parameters and stochastic noises). The unscented Kalman filtering algorithm is utilized to improve accuracy and simplify the implementation. The numerical functionality of the proposed scheme is validated via a simulation example; while its practical versatility is challenged by an experimental case study involving the SMA-actuated flexible tail of a bio-inspired ornithopter. Results demonstrate successfulness of the scheme for online estimation and identification purposes, as well as promising applicability to adaptive nonlinear model-based control.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".