Dynamic Modelling and Predictive Control for Insect-Like Flapping Wing Aerial Micro Robots
Bibliographic record
Abstract
The outstanding potential capability of flapping-wing aerial micro robots to perform gamut [sic] of applications ranging from indoor and confined space missions to perilous environment explorations elevates them from conventional fixed and rotary wing micro aerial vehicles. Despite the remarkable progress in development of manufacturing paradigms to fabricate an at-scale insect-like aerial micro robot, the existing methods are still incompetent to mimic even the most basic maneuvers [sic] of the flying insects. This incompetency comes from technological limitations in terms of size and power density as well as lack of thorough insight into the complex neuromuscular actuation mechanism of the insects' wing. These limitations raise the motivation to develop a simulation framework to be used to analyze the stability and flight dynamics of the insect-like aerial micro robots, and provide a means by which the controller design for these systems could be accomplished. This thesis describes the development of such simulation framework in the context of dynamic modelling and controller design. A consistent set of dynamic and kinematic equations of motion are developed, and the application of the model predictive control strategy for insect-like flapping wing aerial micro robots is investsigated.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".