Prescribed Performance Resilient Motion Coordination With Actor–Critic Reinforcement Learning Design for UAV-USV Systems
Jawhar Ghommam, Maarouf Saad, Mohammad Habibur Rahman, Quanmin Zhu · IEEE Transactions on Artificial Intelligence · 2025
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.