Información de la Revista

IET Control Theory & Applications

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Factor de Impacto:
2.6
Editor:
IET
ISSN:
1751-8644
Vistas:
25671
Seguidores:
0

Solicitud de Artículos

IET Control Theory & Applications is an academic journal published by IET. (ISSN 1751-8644, impact factor 2.6).

Aims and Scope IET Control Theory & Applications is devoted to control systems in the broadest sense, covering new theoretical results and the applications of new and established control methods. Among the topics of interest are system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation. The scope encompasses technological, economic, physiological (biomedical) and other systems, including man-machine interfaces. Most of the papers published deal with original work from industrial and government laboratories and universities, but subject reviews and tutorial expositions of current methods are welcomed. Correspondence discussing published papers is also welcomed. Applications papers need not necessarily involve new theory. Papers which describe new realisations of established methods, or control techniques applied in a novel situation, or practical studies which compare various designs, would be of interest. Of particular value are theoretical papers which discuss the applicability of new work or applications which engender new theoretical applications.
Última Actualización Por Dou Sun en

Special Issues

Special Issue on Modelling, Analysis, and Control of Competition Behaviour in Multi-Agent Systems Día de Entrega: 2026-10-30 Multi-agent systems consist of multiple agents equipped with sensing, communication, and actuation capabilities. In recent years, modelling, analysis, and control of such systems have attracted great attention in the field of control theory and engineering. While cooperative behaviours such as consensus have been extensively studied, the competition behaviour among agents remains relatively underexplored. As one of the fundamental modes of interaction, competition behaviour is closely linked to resource allocation, task assignment, and performance optimization in multi-agent settings. This Special Issue will focus on emerging theories, methods, and applications addressing competition behaviour in multi-agent systems. Topics include modelling, analysis, and control of competition dynamics, incorporating game-theoretic, data-driven, and learning-based approaches. Beyond traditional winners-take-all, Lotka–Volterra, and zero-sum models, the issue aims to explore new directions such as Nash equilibrium seeking, reinforcement learning, hybrid competition–cooperation systems, and robust/adversarial interactions. Applications span from robotics and autonomous systems to energy management, transportation networks, and computational social systems. The Special Issue targets both academic researchers and industrial practitioners, providing a platform to share advances that bridge control theory, artificial intelligence, and complex systems. Topics of interest for this call for papers include but are not restricted to: Modelling and identification of competition behaviour in multi-agent systems; Nonlinear, hybrid, and switching competition–cooperation dynamics; Game-theoretic, evolutionary, and mean-field approaches to competitive control; Data-driven, reinforcement learning, and adaptive methods for competitive agents; Stability, convergence, and performance analysis of competitive networks; Design, analysis, and applications of winners-take-all and Lotka–Volterra models; Zero-sum and non-zero-sum games for distributed control; Robustness, security, and adversarial behaviour in competitive multi-agent systems; Applications in robotics, smart grids, transportation, and socio-economic systems. Guest Editors: Dr. Yinyan Zhang Jinan University, China Dr. John Sum National Chung Hsing University, Taiwan Dr. Shuai Li University of Oulu, Finland Dr. Giuseppe Carlo Calafiore Politecnico di Torino, Italy Dr. Vasilios N. Katsikis National and Kapodistrian University of Athens, Greece Keywords: Artificial Intelligence; Nash Equilibrium; Reinforcement Learning.
Última Actualización Por Dou Sun en

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