Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование
Decentralized Clustering for Adaptive Control in Dynamic Multi-Agent Systems. / Erofeeva, V.; Pankov, V.; Smetanina, V.; Rodríguez-Cortés, H. (редактор).
в: IFAC-PapersOnLine, Том 59, № 14, 2025, стр. 139-144.Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование
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TY - JOUR
T1 - Decentralized Clustering for Adaptive Control in Dynamic Multi-Agent Systems
AU - Erofeeva, V.
AU - Pankov, V.
AU - Smetanina, V.
A2 - Rodríguez-Cortés, H.
N1 - Export Date: 16 February 2026; Cited By: 0; Conference name: 15th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2025; Conference location: Mexico City; Conference date: 2025-07-02 through 2025-07-04
PY - 2025
Y1 - 2025
N2 - The increasing complexity of contemporary control systems, driven by advancements in artificial intelligence, automation, and the widespread use of intelligent systems, demands new control and optimization strategies. Traditional approaches, primarily focusing on individual components or the system as a whole, are increasingly inadequate for control the dynamic interactions and adaptability requirements of these complex systems. This work presents a real-time decentralized clustering method for dynamical multi-agent systems to reduce data dimensionality for adaptive control approaches. Unlike traditional methods that rely on static connections and centralized processing, this method is designed to accommodate dynamic network topologies and varying system states. The method employs compressive sensing and an accelerated consensus approach to acquire aggregated compressed data. A pre-trained neural network then processes this data to forecast cluster centroids. © © 2025 The Authors.
AB - The increasing complexity of contemporary control systems, driven by advancements in artificial intelligence, automation, and the widespread use of intelligent systems, demands new control and optimization strategies. Traditional approaches, primarily focusing on individual components or the system as a whole, are increasingly inadequate for control the dynamic interactions and adaptability requirements of these complex systems. This work presents a real-time decentralized clustering method for dynamical multi-agent systems to reduce data dimensionality for adaptive control approaches. Unlike traditional methods that rely on static connections and centralized processing, this method is designed to accommodate dynamic network topologies and varying system states. The method employs compressive sensing and an accelerated consensus approach to acquire aggregated compressed data. A pre-trained neural network then processes this data to forecast cluster centroids. © © 2025 The Authors.
KW - adaptive learning
KW - adaptive systems
KW - clustering
KW - data aggregation
KW - decentralized systems
KW - Multiagent systems
KW - Adaptive control systems
KW - Cluster analysis
KW - Complex networks
KW - Decentralized control
KW - Decentralized systems
KW - Dynamical systems
KW - Dynamics
KW - Intelligent agents
KW - Intelligent systems
KW - Large scale systems
KW - Learning systems
KW - Neural networks
KW - Real time systems
KW - Adaptive Control
KW - Adaptive learning
KW - Clusterings
KW - Control and optimization
KW - Data aggregation
KW - Decentralised
KW - Decentralized system
KW - Multi agent
KW - Multiagent systems (MASs)
KW - Systems-driven
KW - Multi agent systems
U2 - 10.1016/j.ifacol.2025.12.139
DO - 10.1016/j.ifacol.2025.12.139
M3 - статья в журнале по материалам конференции
VL - 59
SP - 139
EP - 144
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8971
IS - 14
ER -
ID: 148837932