Special Session II
Intelligent Unmanned System: Control, Learning, and Applications 智能无人系统：控制、学习与应用
Introduction:
The rapid advancements in intelligent unmanned systems have revolutionized various fields, including aerospace, robotics, and industrial automation. These systems, powered by cutting-edge control theories, machine learning algorithms, and innovative applications, are increasingly being deployed in complex and dynamic environments. The integration of advanced control strategies, multi-agent coordination, and deep reinforcement learning has enabled unmanned systems to achieve higher levels of autonomy, efficiency, and adaptability. However, significant challenges remain in optimizing their performance, ensuring robustness, defencing potential cyberattacks, and expanding their practical applications.
This special session, titled "Intelligent Unmanned Systems: Control, Learning, and Applications", aims to bring together researchers, engineers, and practitioners to explore the latest developments and future directions in this interdisciplinary field. The session will focus on four core areas: (1) advanced control methodologies for enhancing the stability and precision of unmanned systems, (2) advanced control methodologies for guaranteeing the coordination of unmanned systems under cyberattacks, (3) machine learning techniques, particularly deep reinforcement learning, for enabling autonomous decision-making and adaptive behaviors, and (4) real-world applications of intelligent unmanned systems, such as UAV-based power inspection, autonomous surveillance, and collaborative multi-agent tasks.
智能无人系统的迅猛发展已在航空航天、机器人技术及工业自动化等多个领域引发了革命性变革。这些系统依托前沿控制理论、机器学习算法及创新应用，日益被部署于复杂且动态多变的环境中。先进控制策略、多智能体协调与深度强化学习的融合，使得无人系统能够实现更高层次的自主性、效率与适应能力。然而，在优化其性能、确保鲁棒性、防御潜在网络攻击及拓展实际应用等方面，仍面临着重大挑战。
本专题为“智能无人系统：控制、学习与应用”，旨在探讨该领域前沿进展与未来方向。议题包括但不限于：（1）提升无人系统稳定性与精度的先进控制方法；（2）网络攻击下无人系统协同控制；（3）基于深度强化学习的自主决策与自适应行为；（4）无人机电力巡检、自主监控及多智能体协同等实际应用。
Organizers:
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Dawei Wu, Hohai University, China Dawei Wu is the Associate Professor, Master’s Supervisor, and Deputy Department Head at the College of Artificial Intelligence and Automation, Hohai University. With a long-standing focus on advanced flight control, multi-agent systems control, and deep reinforcement learning, he has led and participated in numerous research projects, including two funded by the National Natural Science Foundation of China, one by the Aeronautical Science Foundation, and over ten other projects. In recent years, he has published more than ten high-quality papers in internationally renowned journals such as Applied Mathematics and Computation, Journal of the Franklin Institute, and International Journal of Robust and Nonlinear Control. His in-depth theoretical research and practical applications in the modeling and control of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) and UAV-based power inspection have earned him several prestigious awards, including the Second Prize for Science and Technology Award from the Jiangsu Association of Automation, the Third Prize for Scientific Research Achievements from Jiangsu Higher Education Institutions, and the First Prize for Science and Technology Award from the China General Chamber of Commerce. |
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Huiliao Yang, Hohai University, China Huiliao Yang received the B.S. and Ph.D. degrees in automatic control from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2012 and 2019, respectively. She is currently an Associate Professor with the College of Artificial Intelligence and Automation, Hohai University, Nanjing. She has published more than 20 academic papers, including high-quality ones in internationally renowned journals such as IEEE Trans. Cybernetics, IEEE/ASME Trans. Mechatronics, and IEEE/CAA J. Automatica Sinica, and has won the Second Prize of the Science and Technology Award of the Jiangsu Province Association for Automation. Her current research interests include fault-tolerant cooperative control of multiagent systems and its application to UAVs, and path planning for multiple UAVs based on intelligence optimization. |
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Rong Li, Taiyuan University of Technology, China Rong Li received his Ph.D. degree in control theory and control engineering from Nanjing University of Aeronautics & Astronautics, Nanjing, China, in 2018. He is currently an Associate Professor in the College of Electrical and Power Engineering at Taiyuan University of Technology, Taiyuan. He has published more than 20 academic papers in internationally renowned journals such as Neurocomputing, Chinese Journal of Aeronautics, and IMA journal of mathematical control and information. His current research interests include nonlinear control, unmanned aerial vehicles (UAVs), and intelligence optimization. |
Submission Guideline:
Please submit your manuscript via Online Submission System: https://easychair.org/conferences/?conf=rcae2025
Please choose Special Session: Intelligent Unmanned Systems: Control, Learning, and Applications