Cooperative learning‐based practical formation‐containment control with prescribed performance for heterogeneous clusters of UAV/USV
Bibliographic record
Abstract
Summary In this paper, a new approach for formation‐containment control with prescribed performances is introduced for heterogeneous autonomous vehicles involving a cluster of leader unmanned aerial vehicles (UAVs) and follower unmanned surface vessels (USVs). We introduce a two‐layer distributed control system: The upper layer focuses on guiding the UAVs to form a scalable lattice while synchronizing their movement along a predefined path, and the second layer guides the USVs to enter the convex hull formed by the UAVs, ensuring collision‐free operation with static/dynamic objects. To prevent collisions and ensure lattice formation, a set of well‐defined bump functions are utilized in the design of the backstepping control algorithm. Managing virtual controls, we incorporate a nonlinear dynamic surface control (NDSC), while a universal barrier function enhances the convergence of formation tracking errors. Furthermore, each USV employs a cooperative adaptive learning neural network to handle uncertainties in heterogeneous vehicle models. Utilizing the Lyapunov theorem, the stability of the formation‐containment of UAV/USV is achieved, and all signals in the formation‐containment systems are semiglobal uniform ultimate bounded (SGUUB). A simulation example showcases the effectiveness of our proposed approach, highlighting contributions in collision avoidance, synchronization speed, and adaptive learning. Our work advances the heterogeneous formation‐containment literature towards more realistic scenarios with safety‐critical considerations amidst multiple layers of uncertainties and unknown parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".