Robust H∞ Optimal Control for Longitudinal and Lateral Dynamics in Small-Scale Helicopters
Bibliographic record
Abstract
In the field of unmanned aerial vehicle control, the pursuit of robustness and optimality in the presence of uncertainties and disturbances remains a paramount challenge, particularly for small-scale helicopters.This study addresses the robust optimal control of longitudinal and lateral flight dynamics using the mixed sensitivity H∞ norm approach.The focus lies on achieving a control system that not only stabilizes but also excels in performance under various flight conditions, including hovering and translational maneuvers.The adopted methodology commences with the derivation of a mathematical model reflecting the system dynamics, characterized by six degrees of freedom and nonlinearities with inherent unstable coupling dynamics.Subsequent linearization of this model employs a Taylor approximation near an operational point, effectively transforming the complex nonlinear system into a more tractable linear form.To incorporate real-world applicability, this model is augmented with representations of uncertainties and external disturbances, acknowledging the unpredictable nature of aerial environments.The crux of this research lies in the implementation of the mixed sensitivity design method for H∞ feedback control.This approach is meticulously applied to the longitudinal and lateral motion subsystems, with a critical emphasis on maintaining system robustness in the face of the aforementioned uncertainties and disturbances.The evaluation of the controller's efficacy is based on qualitative performance metrics, such as response speed and overshoot characteristics.Simulation results demonstrate that the designed controller adeptly manages the intricate multi-input multi-output system, maintaining commendable control performance even under deterministic disturbances and noise.These findings contribute significantly to the unmanned aerial vehicle field, offering a robust control solution for small-scale helicopters that navigate complex environments.The methodology and results presented here hold promise for broader applications in unmanned aerial systems, where stability and adaptability in uncertain conditions are crucial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".