Adaptive Leader–Follower Formation Control of Underactuated Surface Vessels Under Asymmetric Range and Bearing Constraints
Bibliographic record
Abstract
This paper deals with the problem of leader-follower formation control for a group of underactuated surface vessels with partially known control input functions. In the proposed scheme, the problem is formulated as an adaptive feedback control problem for aLine-Of-Sight (LOS) based formation configuration of a leader and a follower. To account for LOS and bearing angle time-varying constraints, asymmetric barrier Lyapunov functions are incorporated with the control design. Furthermore, in order to alleviate required velocity information on the leader, a reconstruction module is designed to estimate the vector velocity of this leader. This reconstruction is accomplished in finite time with zero error, which allows the injection of accurate estimation into the formation controller. The controller is then developed within the framework of the backstepping technique, with the parametric uncertainties and the unknown gains being estimated by a novel structure identifier. The overall closed-loop system, is proved to be semiglobally uniformly ultimately bounded by Lyapunov stability theory. Furthermore, we show under the proposed control scheme that the constraints requirement on the LOS range and bearing angle tracking errors are not violated during the formation process. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".