概率布尔控制网络稳定的状态翻转控制设计。

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xinrong Yang, Haitao Li
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引用次数: 0

摘要

稳定是现代控制理论中的一个基本问题。在过去的几十年里,人们投入了大量的精力来推导验证概率布尔控制网络(pbcn)全局镇定的充分必要条件。然而,现有文献中尚缺乏系统的方法和一般的标准来探索pbcn的局部稳定和确定其吸引域。基于这一研究缺口,本文研究了PBCNs的局部状态反馈镇定问题,包括概率为1的局部有限时间状态反馈镇定(FTSFS)和分布中的局部状态反馈镇定(SFSD)。首先,构造概率为1的可达集序列,在此基础上,通过设计状态反馈控制器,推导出PBCNs FTSFS的最大吸引域;其次,通过构造一个具有正概率的可达集序列,确定了pbcn的SFSD的最大吸引域;最后,当最大吸引域不是整个状态空间时,设计状态翻转控制,通过最大吸引域实现pbcn的全局FTSFS或SFSD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State-flipped control design for the stabilization of probabilistic Boolean control networks
Stabilization is a fundamental issue in modern control theory. In the past decades, significant efforts have been invested in deriving necessary and sufficient conditions for verifying the global stabilization of probabilistic Boolean control networks (PBCNs). However, systematic methods and general criteria for exploring the local stabilization and determining the domain of attraction of PBCNs are still lacking in the existing literature. Motivated by this research gap, this paper investigates the local state feedback stabilization of PBCNs, including local finite-time state feedback stabilization with probability one (FTSFS) and local state feedback stabilization in distribution (SFSD). Firstly, a sequence of reachable sets with probability one is constructed, based on which, the largest domain of attraction is derived for the FTSFS of PBCNs by designing the state feedback controllers. Secondly, by constructing a sequence of reachable sets with positive probability, the largest domain of attraction is determined for the SFSD of PBCNs. Finally, when the largest domain of attraction is not the whole state space, the state-flipped control is designed to achieve the global FTSFS or SFSD of PBCNs via the largest domain of attraction.
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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