Introducation:
The rapid advancement of multi-agent systems underscores the critical need for efficient distributed optimization and control strategies. Traditional centralized approaches frequently encounter limitations in scalability, real-time adaptability, and robustness within large-scale networked systems, including multi-robot coordination, smart grids, and autonomous fleet formation. Distributed optimization, control, and learning methods provide a promising alternative that enables localized decision-making, reduces communication overhead, and enhances resilience to failures and uncertainties.
This special session convenes researchers and practitioners to explore recent advances in distributed cooperative control, optimization, and learning for multi-agent systems. Topics include distributed cooperative control algorithms, consensus-based optimization, game-theoretic approaches, discontinuous control strategies, and application ns in industrial automation, formation control, and smart grids. This session addresses key challenges—including scalability, convergence guarantees, and robustness in large-scale systems—through discussions of theoretical foundations, computational efficiency, and real-world implementations.
The session provides a platform for sharing innovative solutions that close the theory-practice gap, advancing the state of the art in multi-agent systems. We invite contributions demonstrating novel methodologies, rigorous analyses, or impactful applications. This initiative aims to inspire cross-disciplinary collaborations and accelerate progress in this vital research domain.
Organizer(s):
Xuegang Tan, Hainan University, China
Xuegang Tan received Ph.D. in Control Science and Engineering from Southeast University (Nanjing, China) in 2020. During his doctoral studies, he was a visiting researcher at the University of Groningen, the Netherlands, in 2019 under the China Scholarship Council Studentship. From 2020 to 2023, he served as a Postdoctoral Researcher at Southeast University. In 2023, he joined Hainan University (Haikou, China) as an Associate Researcher and is currently an Associate Professor at the School of Information and Communication Engineering of the university. His research focuses on distributed cooperative control, optimization, and networked games in multi-agent systems, as well as neural networks, complex network dynamics, and hybrid control systems.
Yuanyuan Wu, Hainan University, China
Yuanyuan Wu received the Ph.D. degree from the School of Automation, Southeast University, Nanjing, China, in 2010.She is currently a Professor with the School of Information and Communication Engineering, Hainan University, Haikou, China. Her research interests include complex networks, nonlinear control, machine learning, information fusion, and artificial intelligence.
Please submit your manuscript via Online Submission System
Please choose "Special Session 6. Distributed Cooperative Control, Optimization and Learning of Multi-Agent Systems"