Barrier Lyapunov Function-Based Output Regulation Control of an Electromagnetic Micromirror With Transient Performance Constraint
Bibliographic record
Abstract
This article investigates the controller design problem for an electromagnetic torsional micromirror with guaranteed transient performance constraint. Specifically, the developed solution works under the output regulation framework and utilizes the internal model principle for model parameter uncertainties and general reference trajectories tracking, incorporated with barrier Lyapunov function (BLF) method to prevent the tracking constraint violation. We first formulate the micromirror model as an output feedback system with relative degree two and unknown control coefficient, and further turn it into a lower triangular system with an extension transformation. Then, using the extended internal model design, we transform the output regulation problem of the transformed system into the stabilization problem of an augmented system. Finally, based on the BLF technique, we develop a stabilization controller for the augmented system to realize the asymptotic tracking of reference trajectory with transient performance constraint, where the effects of the main design parameters on the control performances are also investigated. By such a technical treatment, the entire control architecture is independent of the angular velocity information of the micromirror, which reduces the measurement complexity in practical. This feature behind the control scheme is important since the velocity information is difficult to be available in many microelectromechanical systems (MEMS). Moreover, the enhanced transient performance is beneficial for improving the scanning quality of the packaged micromirror system. The developed control solution is verified on an experimental platform using a field programmable gate array (FPGA)-based hardware, where the scanning and imaging applications are both conducted.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".