文献类型：期刊文献

英文题名：Fixed-Time Formation Hunting Control of Multi-Marine Surface Vehicle System Based on a Novel Deep Reinforcement Learning

作者：Bai, Weiwei[1,2]; Wang, Yuanhao[1]; Zhao, Bo[3]; Chen, Dewang[1,4]; D'Ariano, Andrea[5]

机构：[1] Navigation College, Dalian Maritime University, Dalian, 116026, China; [2] Guangdong Ocean University, School of Mechanical Engineering, Zhanjiang, 524091, China; [3] School of Systems Science, Beijing Normal University, Beijing, 100875, China; [4] Fujian University of Technology, School of Transportation, Fuzhou, 350118, China; [5] Roma Tre University, Department of Civil, Computer Science and Aeronautical Technologies Engineering, Rome, 00146, Italy

年份：2026

外文期刊名：IEEE Transactions on Systems, Man, and Cybernetics: Systems

收录：EI(收录号：20260920172957)

语种：英文

外文关键词：Adaptive control systems - Computer control systems - Deep neural networks - Deep reinforcement learning - E-learning - Learning algorithms - Vehicles

外文摘要：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. ? 2013 IEEE.