Fixed-Time Formation Hunting Control of Multi-Marine Surface Vehicle System Based on a Novel Deep Reinforcement Learning
针对多艘水面航行器编队围捕问题，提出一种在线自适应深度神经网络与演员-评论家强化学习结合的固定时间控制方法，确保系统在固定时间内收敛。
In this article, a fixed-time deep reinforcement learning (DRL) formation hunting control problem is investigated for a multi-marine surface vehicle (MSV) system. First, considering the lack of dynamic adaptability caused by the conventional deep neural network (DNN) framework, an online adaptive DNN method is proposed for the high-dimensional multi-MSV system. Second, a novel DRL framework is developed for designing fixed-time formation hunting controllers, which integrates the online adaptive DNNs method with the actor–critic-based reinforcement learning (RL) algorithm. Finally, a nonsmooth fixed-time stability analysis is established for the nonsmooth closed-loop system induced by the DRL-based structure, which rigorously demonstrates that all signals converge within a fixed-time interval independent of initial states. The simulation example demonstrates the practical viability of the presented scheme.