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 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".