Design and experimental evaluation of robust motion synchronization control for multivehicle system without velocity measurements
Bibliographic record
Abstract
Summary This paper investigates the robust motion synchronization problem of a class of multivehicle systems suffering from input disturbances but without velocity measurements. We first evaluate a velocity estimator–based scheme and show the performance limitation of the velocity estimator. We then develop a robust distributed control solution, which includes a passivity filter to inject damping into the system and to yield an output‐feedback stabilizer and a novel continuous disturbance estimator (DE) to achieve disturbance compensation. The solution has three attractive features: (i) both the DE and stabilizer are continuous and have the lowest orders; (ii) the DE can be designed in either the time domain or the frequency domain; (iii) by introducing an ingenious parameter mapping for the DE, it is easy to tune a single parameter to render the steady‐state synchronization and tracking errors sufficiently small. The solution is finally implemented on an experimental platform consisting of four desktop three‐degrees‐of‐freedom helicopters. The results of five control scenarios demonstrate that the platform suffers from severe input disturbances, and that different levels of control accuracy can easily be obtained by tuning the DE parameter.
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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) | 0.000 | 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".