Data-Driven Adaptive Control With Hopfield Neural Network–Based Estimator for Piezo-Actuated Stage With Unknown Hysteresis Input
Bibliographic record
Abstract
In this study, a data-driven adaptive controller (DDAC) is designed for piezo-actuated stages (PASs). The primary objective of the controller design is to achieve high tracking accuracy and ensure system stability in the presence of nonaffine uncertainties and unknown hysteresis in the PAS. The proposed DDAC involves three aspects: 1) description of a PAS as a nonaffine nonlinear discrete-time system with a hysteresis input, 2) design of a DDAC using a full-form dynamic linearization model of the PAS, and 3) development of a Hopfield neural network (HNN)-based estimator to update the unknown parameters of the control system online. The key advantages of this study are that prior knowledge of the system nonaffine uncertainties and an offline model are not required. Moreover, the HNN-based estimator can not only update the parameters of the controller, but it can also estimate the output of a PAS to ensure these parameters are valid. The stability of this control system is proved based on the Lyapunov stability analysis theory. Experimental results obtained using the proposed controller on a PAS are compared with those of the existing control methods to verify the effectiveness of the control method.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".