Effectiveness analysis of ship formation air defence based on deep belief network

Li, Bo; Luo, Haoran; Wang, Yuanxun; Chen, Daqing

doi:10.1049/joe.2019.1201

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Effectively enhancing transmission capabilities, formations have been widely used in the mobile robots and vehicle platoons. [13][14][15][16] In an automated highway system, a platoon of multiple connected vehicles can be controlled to reduce traffic congestion problems, improve traffic throughput, and enhance safety of highway traffic. 17 In addition, the formation of mobile robots 13 is also considered as an effective method to solve the autonomy problem.…”

Section: Introductionmentioning

confidence: 99%

Intelligent train control for cooperative train formation: A deep reinforcement learning approach

Zhang

Zhao

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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Considering the intelligent train control problem in long-term evolution for metro system, a new train-to-train communication-based train control system is proposed, where the cooperative train formation technology is introduced for realizing a more flexible train operation mode. To break the limitation of centralized train control, a pre-exploration-based two-stage deep Q-learning algorithm is adopted in the cooperative train formation, which is one of the first intelligent approaches for urban railway formation control. In addition, a comfort-considered algorithm is given, where optimization measures are taken for providing superior passenger experience. The simulation results illustrate that the optimized algorithm has a smoother jerk curve during the train control process, and the passenger comfort can be improved. Furthermore, the proposed algorithm can effectively accomplish the train control task in the multi-train tracking scenarios, and meet the control requirements of the cooperative formation system.

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