Simple Tuning for an Adaptive and Model-Free Control of Indoor Airships
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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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".