New Insights in Multi-Agent Systems and Intelligent Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1465

Special Issue Editors


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Guest Editor
Department of Computer Science and Automatic Control UNED Juan del Rosal 16, Madrid 28040, Spain
Interests: networked control systems; event-based control; multi-agent systems; control education

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Guest Editor
Department of Computer Sciences and Automatic Control, National University of Distance Education (UNED), 28040 Madrid, Spain
Interests: networked control systems; event-based control; relay feedback systems; control education; machine learning

E-Mail Website
Guest Editor
Department of Computer Sciences and Automatic Control, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal 16, 28040 Madrid, Spain
Interests: process control; machine learning; event-based control; virtual and remote laboratories

Special Issue Information

Dear Colleagues,

Integrating Multi-Agent Systems (MASs) and Intelligent Control allows the development of intelligent distributed systems and applications in complex and dynamic scenarios.  Despite achievements on MASs in the last decade, many real-world challenges still limit MASs on safety, robustness, or generalization. It is necessary to promote the development of this field in combination with intelligent control in practical applications.

This special issue aims to provide a platform for researchers and practitioners to share their latest findings and advancements in multi-agent systems and intelligent control. This issue will encompass a broad range of topics, including but not limited to agent-based modeling, cooperative and distributed control strategies, swarm intelligence, machine learning, and autonomous systems. It aspires to foster interdisciplinary collaboration and promote the exchange of innovative ideas that can significantly impact areas such as robotics, complex networks, autonomous vehicles, smart cities, and industrial automation. By bringing together leading experts in the field, this special issue attempts to advance our understanding of how multi-agent systems can benefit from intelligent control, providing valuable insights that will contribute to the development of more efficient and adaptable systems across a wide range of applications.

Dr. María Guinaldo
Prof. Dr. José Sánchez Moreno
Prof. Dr. Raquel Dormido Canto
Guest Editors

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Keywords

  • agent-based modeling
  • cooperative control
  • distributed control
  • complex networks
  • swarm intelligence
  • machine learning
  • autonomous systems
  • robotics

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Published Papers (2 papers)

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Research

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16 pages, 1249 KiB  
Article
A Distributed Algorithm for Reaching Average Consensus in Unbalanced Tree Networks
by Gianfranco Parlangeli
Electronics 2024, 13(20), 4114; https://doi.org/10.3390/electronics13204114 - 18 Oct 2024
Viewed by 428
Abstract
In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, [...] Read more.
In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, the relative solution is proposed as a finite-time algorithm, which can be included in any application as a preliminary setup routine, and it is well-suited to be integrated with other adaptive setup routines, thus making the proposed solution useful in several practical applications. A special focus is devoted to the integration of the proposed method with a recent Laplacian eigenvalue allocation algorithm, and the implementation of the overall approach in a wireless sensor network framework. Finally, a worked example is provided, showing the significance of this approach for reaching a more precise average consensus in uncertain scenarios. Full article
(This article belongs to the Special Issue New Insights in Multi-Agent Systems and Intelligent Control)
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Review

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20 pages, 5234 KiB  
Review
Aggregators Used in Fuzzy Control—A Review
by Mirosław Kozielski, Piotr Prokopowicz and Dariusz Mikołajewski
Electronics 2024, 13(16), 3251; https://doi.org/10.3390/electronics13163251 - 16 Aug 2024
Viewed by 649
Abstract
An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be [...] Read more.
An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be considered and mathematical description and data-processing systems better adapted to them. Aggregators are a group of tools used in solving the aforementioned decision problems, including within fuzzy systems. Aggregating functions are a useful tool mainly in those artificial intelligence systems with problems arising from incomplete data. The aim of this article is to review and describe existing aggregators used in fuzzy control in terms of their usefulness and limitations of their use. Particular attention is paid to the criteria for matching a suitable aggregator to a particular computational problem. This represents an important step towards the further use of this group of technologies in electronic devices and IT systems. Full article
(This article belongs to the Special Issue New Insights in Multi-Agent Systems and Intelligent Control)
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