Recent Developments in Control and Optimization for Multi-Agent Systems

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1802

Special Issue Editors


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Department of Mathematics, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: robust control; stabilization; fractional-order systems; multi-agent systems
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School of Electrical Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Interests: systems and control theory; fuzzy systems
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Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan
Interests: robust control; neural networks
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Department of Computer Science, Faculty of Science, University of Split, Ruđera Boškovića 33, HR-21000 Split, Croatia
Interests: artificial intelligence; intelligent agents; machine learning; computer science education; interdisciplinary research in education; engineering applications of intelligent technologies
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Special Issue Information

Dear Colleagues,

Multi-agent systems are composed of multiple autonomous agents that interact with each other and their environment to achieve individual or collective goals. The study of these systems spans across various fields, such as artificial intelligence, robotics, economics, and social sciences. Researchers investigate multi-agents to understand the dynamics of agent interactions, the emergence of collective behavior, and the optimization of system-level objectives. The complexity and interdependencies within multi-agent systems make them an intriguing and challenging area of research with broad applications in real-world scenarios. Such systems can exhibit complex behaviors and dynamics, as the interactions between agents can give rise to nonlinear and unpredictable system-level phenomena. Understanding and managing the complexity of multi-agent systems represent a significant research area. Cooperative control is crucial in the investigation of multi-agent systems; it includes consensus, formation control, containment control, and so on.

The objective of this Special Issue is to showcase articles that highlight innovative advancements and outcomes in the theory and implementation of control and optimization algorithms for multi-agent systems. The issue will specifically concentrate on advanced and unconventional approaches, introducing noteworthy innovations in both theoretical foundations and practical applications.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) Multi-agent systems and their applications;

(2) Consensus of multi-agent systems;

(3) Formation control of multi-agent systems;

(4) Optimization techniques for multi-agent systems;

(5)  Reinforcement Learning for multi-agent systems;

(6) Bipartite consensus for multi-agent systems;

(7) Cluster consensus for multi-agent systems.

Dr. Parivallal Arumugam
Dr. Ramasamy Kavikumar
Dr. S. A. Karthick
Prof. Dr. Saša Mladenović
Guest Editors

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Keywords

  • robust control
  • multi-agent systems
  • cooperative control
  • graph theory
  • consensus
  • formation control

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Published Papers (1 paper)

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Research

17 pages, 3828 KiB  
Article
An Adjustable Parameter-Based Robust Distributed Fault Diagnosis for One-Sided Lipschitz Formation of Clustered Multi-Agent Systems
by Ailin Barzegar and Afshin Rahimi
Electronics 2024, 13(14), 2759; https://doi.org/10.3390/electronics13142759 - 13 Jul 2024
Viewed by 1083
Abstract
This paper addresses the challenge of distributed fault diagnosis in the context of the one-sided Lipschitz formation of agents. Each agent integrates an observer to detect and estimate both linear and non-linear faults in its attitude control subsystem. A robust design configuration is [...] Read more.
This paper addresses the challenge of distributed fault diagnosis in the context of the one-sided Lipschitz formation of agents. Each agent integrates an observer to detect and estimate both linear and non-linear faults in its attitude control subsystem. A robust design configuration is also developed to account for external perturbations. The robust observer utilized in this study is an unknown input observer (UIO), designed to mitigate the impact of disturbances on fault and state estimation errors. The observer’s parameters are determined using linear matrix inequalities (LMIs). Furthermore, a UIO incorporating an adjustable parameter (AP) is introduced to enhance fault diagnosis accuracy. Simulation results for two satellite clusters, consisting of five satellites with varying dynamics due to external disturbances, are presented to validate the approach. Instead of equipping every agent with an observer, specific agents can be equipped with observers to detect faults throughout the constellation, thereby reducing computational demands in configurations with numerous agents. Finally, a comparison is made between the proposed AP-based UIO and a standard UIO. The comparison findings reveal a noteworthy average of a substantial 56.61% reduction in root mean square error (RMSE) employing AP-based UIO compared to the utilization of standard robust UIO. Full article
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