A novel brain-inspired approach based on spiking neural network for cooperative control and protection of multiple trains

The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collab...

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Vydané v:	Engineering applications of artificial intelligence Ročník 127; s. 107252
Hlavní autori:	Zhang, Zixuan, Song, Haifeng, Wang, Hongwei, Tan, Ligang, Dong, Hairong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.01.2024
Predmet:	Brain-inspired control Cooperative operation Railway train control Spiking neural networks Brain-inspired control Cooperative operation Railway train control Spiking neural networks
ISSN:	0952-1976, 1873-6769
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Popis
Shrnutí:	The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collaborative functionalities of various brain regions, harnessing the power of spiking neural networks. The controller’s primary tasks include reference velocity tracking, cooperative control, and active protection, with a special focus on cooperative protection within distinct operational modes. Furthermore, the fundamental principles of incorporating spiking neural networks into train control, such as coding and decoding mechanisms, are expounded. The overarching controller is partitioned into two principal functional segments. The first segment involves emulating the prefrontal cortex (PFC) for reference velocity tracking and active protection against overspeed and collisions through motor control and movement planning. The second segment employs a cerebellum-inspired network for cooperative control. Additionally, the brain-inspired network introduced in this study undergoes training utilizing biologically-inspired mechanisms, incorporating dopamine and pertinent teaching signals to facilitate realistic synaptic modifications. Simulation results in several scenarios validate the proposed approach.
ISSN:	0952-1976 1873-6769
DOI:	10.1016/j.engappai.2023.107252