Towards Computationally Efficient NMPC Design with Stability Guarantee for Learning-Based Dynamic Models: A Case Study of UAVs
Bibliographic record
Abstract
This paper proposes a novel computationally efficient nonlinear model predictive controller (NMPC) for learning-based models. The proposed NMPC scheme uses a hybrid model of the dynamic system, including a nominal derived model and a learning-based model that compensates for the incomplete knowledge of the system, i.e., unmodeled dynamics. The NMPC is designed with a tailored cost function that takes into account the learned-dynamics of the system. The cost function is formulated without stabilizing terminal conditions required for stabilization. Moreover, the proposed scheme facilitates the computation of the shortest possible stabilizing prediction horizon that guarantees the asymptotic stability of the closed-loop system. The proposed scheme is applied to an unmanned aerial vehicle (UAV) for validation. The performance of the proposed scheme is investigated through extensive numerical simulations and compared against the state-of-the-art traditional NMPC and traditional learning-based NMPC schemes proposed in literature. The results show superior trajectory tracking performance of the proposed learning-based NMPC scheme at short prediction horizons.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".