Learning-Driven Control and Fault Diagnosis for High-Reliability Intelligent Systems

Introduction: With the increasing complexity, autonomy and safety requirements of intelligent manufacturing, robotics, aerospace equipment, smart grid and other fields, high-reliability intelligent systems have become an essential support for the safe operation of critical infrastructure. Intelligent unmanned systems usually operate in dynamic, uncertain and adversarial environments, and are vulnerable to strong interference, coupled faults, cyberattacks, sensor deception and communication disturbances. It is urgent to improve their stable control, fault identification and fault-tolerant self-healing capabilities.The integration of learning-driven control and data-driven fault diagnosis has become a core approach to enhance the adaptability, autonomy and operational reliability of intelligent systems. Under complex nonlinear coupling, working condition migration and open environmental disturbances, there still remain numerous theoretical and technical challenges in realizing autonomous fault perception, adaptive regulation and safety defense of high-reliability intelligent systems.

This special session welcomes original research papers and review papers in the following topics:

1. Learning-driven advanced control: Data-driven control, reinforcement learning control, adaptive control and robust intelligent optimal control for multi-agent systems and nonlinear uncertain systems;
2. Intelligent fault diagnosis and security defense: Fault detection, isolation, traceability identification, fault-tolerant and active defense strategies against sensor failure, network intrusion, data deception and other threats;
3. Machine learning and health management: Applications of deep learning, reinforcement learning and self-supervised learning in system autonomous decision-making, adaptive regulation and full-lifecycle health monitoring;
4. Practical engineering applications: Algorithm verification and system engineering practices in intelligent manufacturing, unmanned platforms, aerospace, smart grids, rail transit and other scenarios.

 

Organizers:

	Yiyang Chen, Soochow University, China Yiyang Chen received his bachelor’s degree from Imperial College London and his Ph.D. from the University of Southampton, UK. He is currently an Associate Professor and Outstanding Young Scholar in the Department of Automation, School of Mechanical and Electrical Engineering, Soochow University, as well as the Academic Leader of Low-Altitude Technology and Engineering. He has been listed among the World’s Top 2% Scientists, and has received numerous honors including Jiangsu Provincial Innovation and Entrepreneurship Doctor, Member of Jiangsu Provincial Science and Technology Mayor Group, Enterprise Science and Technology Vice Director of Jiangsu Province, and Innovation and Entrepreneurship Leading Talent of Suzhou High-tech Zone. He has presided over a number of research projects, including the Youth Program of the National Natural Science Foundation of China, the Youth Program of Jiangsu Provincial Natural Science Foundation, the Forward-looking Applied Basic Research Project of Suzhou City, and the Pilot Pre-research Project of Wuxi Industrial Research Institute. He is a Member of IEEE, a Member of the Chinese Association of Automation, and a Committee Member of the Technical Committee on Data-Driven Control, Learning and Optimization of the Chinese Association of Automation. He also serves as an Editorial Board Member of Symmetry, Intelligence & Robotics, and Embodied Intelligence and Robotics.
	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.
	Sudao He, The Hong Kong University of Science and Technology, Hong Kong, China Sudao He, Ph.D. in Engineering, is currently a Postdoctoral Researcher in the Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology. His research focuses on AI-enabled structural perception and health monitoring, with interests spanning multimodal sensing, geometric machine vision, distributed fiber-optic sensing, data-efficient learning, uncertainty quantification, and multimodal large language models. His work has been published in leading journals. He has led two Research Talent Hub projects funded by the Hong Kong Innovation and Technology Commission and received the Best Oral Presentation Award at RCAE 2025. He also serves as an Associate Editor of IEEE Transactions on Instrumentation and Measurement, where he has received multiple Outstanding Reviewer and Outstanding Associate Editor Awards.

Submission Guideline:

Please submit your manuscript via Online Submission System: https://easychair.org/conferences/?conf=rcae2026
Please choose "Special Session 8"