Prescribed Performance Resilient Motion Coordination With Actor–Critic Reinforcement Learning Design for UAV-USV Systems
Bibliographic record
Abstract
In this paper, we develop a virtual vehicle scheme to solve the coordination control problem under denial-of-service (DoS) attacks for heterogeneous vehicles. This system includes an unmanned surface vessel (USV) in distress, sharing kinematic data, and a helicopter receiving data from the latter through wireless communication. Specifically, we carefully develop an estimator to model the unmeasurable states of the USV in the presence of DoS attacks. The virtual vehicle concept is then utilized to generate a velocity reference output for the helicopter to follow. To achieve preset tracking performances, the cascade structure of the helicopter is exploited, where the backstepping control strategy is used via a barrier Lyapunov function. To handle input constraints, auxiliary systems are built to bridge the association between input saturation errors and performance constraints. Furthermore, to mitigate the saturation effect of bounded inputs and model uncertainties in the attitude dynamics, a fixed-time reinforcement learning (FT-RL) control algorithm is designed according to actor-critic strategy. Stability analysis is thoroughly studied with the help of Lyapunov stability where sufficient conditions for the whole closed-loop system have been obtained. Numerical simulations have been shown to validate the proposed coordination strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".