Intuitive Physics-Driven Approach: Design and Calibration of a Flight Controller for Indoor Blimps
Bibliographic record
Abstract
Abstract This research addresses the challenges of defining and tuning a flight controller for uncrewed aerial vehicles (UAVs), mainly, a lighter-than-air vehicle, like a blimp; a task often fraught with complexity for UAV designers. These challenges typically involve several iterations in both simulation and real-world settings that require the identification of the dynamic model. In this paper, we introduce a novel, but simple approach that harnesses intuitive physics principles to streamline the process of tuning and to ensure the safeness and robust path tracking for indoor blimps. Our method involves a threefold strategy: 1. A basic control rooted in Sliding Mode Control (SMC) with a saturation term that permits to achieve the motion in the three axes (X, Y and Z), while defining maximum cruising speeds and maximum desired control forces; used to complete the operation of path tracking and hovering. 2. A recursive simple moving average (SMA) term for the applied control forces is incorporated to the previous strategy to minimize steady-state error in real-time for altitude control, which can compensate for changes in the weight of the blimp. 3. A swing stabilizer mechanism designed to dampen oscillations around naturally occurred pitch and roll angles for enhanced stability. Experimental results validate the efficacy and simplicity of our approach, demonstrating robustness, rapid deployment and good path accuracy.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".