QUASIPASSIVITY-BASED ROBUST NONLINEAR FLAP POSITIONING CONTROL USING SHAPE MEMORY ALLOY MICRO-ACTUATORS
Bibliographic record
Abstract
This paper proposes a quasipassivity-based robust nonlinear control law ensuring position control of a rotary flap by means of an antagonist-type shape memory alloy microactuator. The control system employs variable-structure control to obtain robust performance, phase-lead compensation to quasipassivate the shape memory alloy dynamics and quasipassivity-based analysis to warrant robust ultimate boundedness of system trajectories. The feedback connection of the two paths leads to ultimate boundedness of tracking error trajectories of the plant despite uncertainties in the dynamic loads affecting the leading edge flap and in the friction found in the actuator. Since accurate numerical simulations and development of new concepts of microactuators based on shape memory alloys require a tractable, constitutive law accurately describing the relationship between force, displacement and temperature in the material, the paper also presents a hybrid micro-macro-mechanical shape memory alloy constitutive model. This model is based on a combination of structural modeling on a microscopic scale and transformation kinetics modeling on a macroscopic scale. The proposed control law and hybrid micro-macro-mechanical model are placed in closed-loop by means of numerical simulations that demonstrate the validity of the nonlinear control scheme.
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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 | 0.000 |
| 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 | 0.001 |
| 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".