Learning-based Control and Fault Diagnosis in Reliable and Intelligent Systems
可靠智能系统中基于学习的控制和故障诊断

Introduction:

The reliability of intelligent systems is of vital importance in fields such as industrial manufacturing, robotics, and aerospace. With their growing deployment in complex, dynamic environments for critical missions, ensuring the dependability of these systems has become a top priority. The integration of advanced learning-based control algorithms with intelligent fault diagnosis strategies has emerged as a crucial research direction to enhance both performance and fault tolerance. Nevertheless, substantial challenges persist in developing systems capable of autonomous fault detection, adaptation to changing conditions, and maintenance of precise operation under various security threats while executing complex tasks.
This special session, titled 'Learning-based Control and Fault Diagnosis in Reliable and Intelligent Systems', aims to convene researchers, engineers, and practitioners to investigate cutting-edge developments and future directions in learning-based control and diagnosis for reliable intelligent unmanned systems. The session will concentrate on the following key areas:
(1). learning-based control methodologies designed to achieve collaborative system stability, adaptability, and precision,
(2). intelligent fault diagnosis strategies that ensure the coordination and functionality of unmanned systems under diverse security challenges, including cyber-physical attacks, sensor spoofing, communication jamming, and physical tampering,
(3). machine learning techniques for autonomous decision-making, adaptive behaviors, and system health monitoring, with a particular focus on deep reinforcement learning and fault diagnosis algorithms that maintain control accuracy,
(4). real-world applications of reliable and intelligent systems, such as precision inspection, autonomous surveillance, and cooperative missions with fault-tolerant requirements.

智能系统的可靠性在工业制造、机器人和航空航天等领域至关重要。随着智能系统越来越多地部署在复杂、动态的环境中执行关键任务，确保这些系统的可靠性已成为重中之重。将先进的基于学习的控制算法与智能故障诊断策略相结合已成为提高性能和容错能力的重要研究方向。然而，在开发能够自主检测故障、适应不断变化的条件并在执行复杂任务的同时在各种安全威胁下保持精确运行的系统方面仍然存在巨大的挑战。
本次专题会议的主题为“可靠智能系统中基于学习的控制和故障诊断”，旨在召集研究人员、工程师和从业人员，共同探讨可靠智能无人系统基于学习的控制和诊断的前沿发展和未来方向。会议将集中在以下关键领域：
(1). 旨在实现协作系统稳定性、适应性和精确性的基于学习的控制方法；(2). 确保无人系统在各种安全挑战下的协调性和功能性的智能故障诊断策略，包括网络物理攻击、传感器欺骗、通信干扰和物理篡改；(3). 用于自主决策、自适应行为和系统健康监测的机器学习技术，特别关注保持控制精度的深度强化学习和故障诊断算法；(4). 可靠智能系统的实际应用，例如精密检查、自主监视和具有容错要求的合作任务。

Organizers:

	Yiyang Chen, Soochow University, China Yiyang Chen received the M.Eng. degree from Imperial College London, London, U.K., in 2013, and the Ph.D. degree from the University of Southampton, Southampton, U.K., in 2017. After that, he worked as a Research Fellow in control systems (2017–2018) and in traffic signal control (2018–2020) at the University of Southampton. He joined the School of Mechanical and Electrical Engineering, Soochow University, in 2020, as an Associate Professor. He has published several papers in top control conferences and journals. His research interests include iterative learning control, optimization, artificial intelligence, image processing, and robotic systems.
	Shenghui Guo , Suzhou University of Science and Technology, China Shenghui Guo, born in June 1983, is an associate professor and deputy director of the Electrical Engineering Department. He obtained his Ph.D. from Tongji University and completed his postdoctoral research at Nanjing University of Aeronautics and Astronautics. During October 2023 to October 2024, he was a visiting scholar at Korea University. An IEEE member and active in multiple academic societies, Guo has been recognized with various honors, including Excellent Young Backbone Teacher and Excellent Teacher. His research focuses on intelligent connected vehicle control, multi-agent systems, cyber-physical systems, and state estimation and fault diagnosis. In the past five years, he has published nearly 50 academic papers, over 20 of which are SCI-indexed, and hosted 15 scientific research projects while supervising 13 master's students.
	Zhaomin Lv, Shanghai University of Engineering Science, China Zhaomin Lv obtained his bachelor's degree in automation from East China University of Science and Technology in 2012. He received his Ph.D. degree in control science and engineering from East China University of Science and Technology in 2017. He won the Shanghai Youth Science and technology talents sailing program in 2018. Currently, he is an associate professor at the School of Urban Railway transportation, Shanghai University of Engineering Science. His research interests include fault diagnosis and machine learning.

Submission Guideline:

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