Neural Network-Based Switching Output Regulation Control for High-Speed Nano-Positioning Stages
Why this work is in the frame
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Bibliographic record
Abstract
This study establishes a high-speed nano-positioning stage composed of a symmetrically driven structure with multiple parallel-bonded thin piezoelectric ceramic layers capable of performing micro- or nano-scale manipulations. Accordingly, a neural-network-based switching output regulation controller (NN-SORC) was developed to compensate for the associated hysteresis nonlinearity. To address the challenges of slow floating-point computation speeds and low compilation efficiency, a closed-loop control system with a field-programmable gate array–central processing unit (FPGA-CPU) dual-layer data-processing framework was developed. A feedback linearization method was designed to linearize the hysteresis nonlinearity of the framework, resulting in a switching-tracking error system. With the assistance of Lyapunov theory and an average dwell time technique, sufficient conditions were derived to ensure the asymptotic stability of the NN-SORC governing closed-loop system using the switching reference signals often encountered in realistic micro-/nano-scale detection and manufacturing processes. Finally, extensive comparative experiments were conducted to verify the effectiveness and superiority of the proposed NN-SORC scheme.
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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 it