Nonlinear Robust Attitude Tracking Control of a Table-Mount Experimental Helicopter Using Output Feedback
Bibliographic record
Abstract
This paper proposes a robust attitude tracking controller for a table-mounted experimental helicopter that has three rotational degrees-of-freedom and only equips angular position sensors. The proposed controller can achieve output-feedback attitude tracking of the pitch and elevation channels of the helicopter. The experimental platform is subjected to model uncertainties, coupling effects, and equips with an independent active disturbance system, which have effectively examined the robustness of proposed controller. The control law includes a second-order auxiliary system to generate filtered error signals, and a discontinuous uncertainty and disturbance estimation (UDE) term to compensate the model uncertainties and external disturbances. A Lyapunov-based stability analysis shows the semiglobal asymptotic tracking ability of the proposed controller. The experimental results further demonstrate that the proposed method can achieve equivalent dynamic and static performance compared to other high-performance state-feedback methods, and even when the initial position is away from design point, it also gives more consistent responses than other linear methods.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".