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面向一种网络物理多智能体应用的联盟式分布式模型预测控制视角。

A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application.

机构信息

Department of Automatic Control and Applied Informatics, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.

出版信息

Sensors (Basel). 2021 Jun 11;21(12):4041. doi: 10.3390/s21124041.

Abstract

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

摘要

随着当前技术的发展和信息的进步,越来越多的物理系统通过通信网络实现了互联和连接。本工作的目的是开发一种适用于控制网络物理多智能体系统的联盟分布式模型预测控制(C-DMPC)策略。这项工作的动机是通过结合经典 DMPC 和联盟 MPC 的优势,设计一种具有灵活控制架构的新型算法。使用由四个通过单向通信拓扑连接的动态耦合子系统组成的测试场景来获得仿真结果。所得到的结果表明,当局部优化问题的可行性丧失时,通过在相邻代理之间形成联盟,可以解决这一缺点并保持整个系统的功能。这些发现成功证明了所提出的联盟 DMPC 方法的效率和性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67b4/8230880/4b1ca3b160f2/sensors-21-04041-g001.jpg

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