Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method
Bibliographic record
Abstract
Performance and stability are demonstrated for a nonlinear path-following guidance method for unmanned air vehicles. The method was adapted from a pure pursuit-based path following, which has been widely used in ground based robot applications. The method is known to approximate a proportional-derivative controller when following a straight line path, but it is shown that there is also an element of anticipatory control that enables tight tracking when following curved paths. Ground speed is incorporated into the computation of commanded lateral acceleration, which adds an adaptive capability to accommodate vehicle speed changes due to external disturbances such as wind. Asymptotic Lyapunov stability of the nonlinear guidance method is demonstrated when the unmanned air vehicle is following circular paths. The adaptive nature of the guidance method makes its stability independent of vehicle velocity. The stability analysis is also extended to show robust stability of the guidance law in the presence of saturated lateral acceleration, which is an inherent limitation of flight vehicles. Flight tests of the algorithm, using two small unmanned air vehicles, showed that each aircraft was controlled to within 1.6 m root mean square when following circular paths. The method was used to perform a rendezvous of the two aircraft, bringing them into very close proximity, within 12 m of along track separation and 1.4 m root mean square relative position errors.
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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) | 0.001 | 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".