Nonlinear MPC Without Terminal Costs or Constraints for Multi-Rotor Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This letter proposes a novel NMPC for multi-rotor aerial vehicles which is designed without stabilizing terminal costs or constraints in its cost function for stabilization. A growth bound sequence is derived from a tailored running cost to ensure the closed-loop stability and provide a measure of the performance of the proposed NMPC scheme. Furthermore, it facilitates the computation of a stabilizing prediction horizon that guarantees the asymptotic stability of the system. The performance of the proposed scheme is investigated through two sets of numerical simulations and compared against the traditional NMPC scheme for the application as proposed in (Kamel et al., 2017). The results show superior performance of the proposed NMPC scheme in terms of tracking accuracy, convergence rate, and computation time.
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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) | 0.001 | 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