AN AUTOPILOT DESIGN FOR THE LONGITUDINAL DYNAMICS OF A LOW-SPEED EXPERIMENTAL UAV USING TWO-TIME-SCALE CASCADE DECOMPOSITION
Bibliographic record
Abstract
This paper discusses a cascade decomposition method for two-time-scale systems. We decompose the two-time-scale system into slow and fast subsystems connected in cascade form. With the proposed decomposition method, fast and slow low-order subsystems can be easily extracted from the two-time-scale full order system. The longitudinal dynamics of a low speed experimental UAV is used to illustrate the proposed method. An altitude hold control structure is constructed to allow sequential designs based on the lower order fast and slow models using classical designs. The structure contains two control loops. The inner loop is for altitude rate regulation. In this control loop, we use combination of altitude rate and pitch angle as the feedback signals. A fast controller is designed to satisfy the high frequency requirements using the fast model. Following the results of the fast design, three control gains are designed to satisfy the low frequency requirements using the slow models. The design results of the inner loop are used to design the outer altitude control loop. Frequency domain analysis and computer simulations confirm the success of the proposed decomposition method and verify the effectiveness of the control law using the proposed control structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".