Simple Tuning for an Adaptive and Model-Free Control of Indoor Airships
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper addresses the challenges involved in designing and tuning flight controllers for uncrewed aerial vehicles, focusing on the complexities specific to lighter-than-air vehicles, often referred to as blimps. Traditional approaches often require numerous iterations in both simulation and real-world environments to identify dynamic model parameters (such as mass, inertia, and damping) and to fine-tune controller gains to achieve stable flights. In contrast, we propose a streamlined methodology that leverages intuitive physics principles to simplify the control, tuning, and stabilization process, ensuring safe and robust path tracking for indoor blimps. Our approach incorporates sliding mode control (SMC) with a saturation term to regulate translational motion across all three axes as well as yaw, while limiting both cruising speeds and control forces. Additionally, we employ a recursive simple moving average (SMA) mechanism that reduces steady-state errors in real-time, enabling altitude control in response to weight changes and adjusting speed to compensate for drag. To further enhance stability, an SMA-based stabilization technique dampens oscillations that naturally occur around the pitch and roll axes, improving performance during both hovering and flight. Experimental results validate the effectiveness of this method, demonstrating its robustness, rapid deployment, path accuracy, and oscillation control, all with minimal tuning effort.
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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) | 0.000 | 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) | 0.000 | 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