Evaluation of Linear Control Law Synthesis Methods for Small-Scale Supersonic Uncrewed Aerial Vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of small-scale supersonic uncrewed aerial vehicles (SSUAV) remains elusive. Such systems’ control is especially problematic; no consensus exists on which control architecture to employ. This paper evaluates control and state estimation methods for SSUAV and discusses the defined comparison between varied control systems. Linear control methods such as ��∞ and linear quadratic regulators are evaluated in continuous time and are derived and tested. A comparison of the continuous-time control systems for linear control systems with the same linear Kalman state estimator was assessed using linear dynamic inversion and classical linear control methods formulations with optimal control mechanisms. Initial results show that attitude estimation imposes challenges due to the need to linearize a nonlinear UAV model. Issues when predicting nonlinear trajectories occur in coupled states such as roll and yaw angles. This is not impacted by the control design used but is a universal reaction. No significant difference exists between the performance of any control designs presented except in the derivation of the control system linear gains ��. The linear dynamic inversion provides no benefit and complicates the control law further by requiring more information about the model than classical design methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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