Adaptive Incremental Nonlinear Dynamic Inversion Control with Guaranteed Stability for Aerial Manipulators
Bibliographic record
Abstract
This paper introduces an adaptive Incremental Nonlinear Dynamic Inversion (INDI) control methodology with guaranteed stability for a highly maneuverable unmanned aerial manipulator (UAM) designed to operate under demanding conditions, such as rapid arm movements and varying manipulated payloads. This work extends previous work on the control of aerial manipulators by addressing control effectiveness uncertainties. The stability bounds of the inertia matrix within the control effectiveness matrix are derived through a detailed eigenvalue analysis, ensuring that the eigenvalues consistently remain within a specified stability threshold. The proposed methodology ensures both stability and control responsiveness by dynamically adjusting the inertia parameters of the control effectiveness matrix within stability-guaranteeing limits. The methodology is validated through extensive simulation tests showing that the proposed adaptive INDI controller outperforms previous UAM controllers, effectively coping with disturbances caused by varying grasped payloads/masses and extended arm movements with guaranteed stability.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".