Prof. Xiangpeng Xie
Vice Dean of the School of IoT
Nanjing University of Posts and Telecommunications, China

Speech Title: Industrial Cyber-Physical Systems Driven by Explainable Artificial Intelligence

Abstract: In the context of green industrial development, information depth perception, intelligent optimization decision-making, precise control, and self-learning are crucial for improving the control level of industrial cyber physical systems. The purpose of this report is to explore the following research topics: data-driven models and evolutionary representations of industrial cyber physical systems for dynamic interaction of all factors of production, innovative data and expert knowledge extraction and integration paradigms, and the construction of a variable topology self-organizing hierarchical hybrid fuzzy modeling framework; Construct an elastic memory event triggered communication mechanism based on the coupling mechanism between "communication mechanism control performance network resilience" of industrial cyber physical systems based on TSN technology; Aiming at the problem of online extraction of spatiotemporal characteristics, fatigue detection technology based on self-organizing interval type-2 fuzzy system and transferable class statistics and multi-scale feature fuzzy approximation technology are proposed. Then, a variable weight switching mechanism that can quantitatively describe the evolution trend of energy function at multiple sampling moments is designed to achieve interactive enhancement functions such as flexible switching fuzzy control. Finally, a brief introduction is given to the preliminary applications of the research results in fields such as intelligent manufacturing and intelligent transportation.

Biography: Prof. Xie has been selected for the National Excellent Young Scientists Fund, and ESI Highly Cited Researcher (Interdisciplinary Field). Awards include the Youth Science and Technology Award from the Chinese Association of Automation, the First Prize in Natural Science (2/5) from the Chinese Association of Automation, the Second Prize in Shanghai Natural Science (3/4), the First Prize in Natural Science (2/4) from China Simulation Federation, and the Most Influential International Academic Paper in China (1/4). The researcher serves as an editorial board member for IEEE TII, TFS, and TCYB journals.

Prof. Kai Ma
Vice Dean of the School of Electrical Engineering
Yanshan University, China

Speech Title: Coordinated Control and Optimization for Smart Grid

Abstract: Smart grid represents a new power system characterized by the deep integration of energy flow, information flow, and business flow. It accommodates a high proportion of renewable energy sources and faces complex load characteristics, necessitating frequent information exchange for coordinated operation of sources, storage, and loads. This report introduces coordinated control and optimization methods for smart grid from three perspectives, including coordinated control methods for distributed energy sources and thermostatically-controlled loads, economic dispatch and pricing strategies considering user comfort, and adaptive information transmission mechanisms for energy systems. Finally, the report provides an outlook on the application of smart grid in green ports.

Biography: Professor Ma Kai is the Vice Dean of the School of Electrical Engineering at Yanshan University, and the Director of the Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education. His research interests focus on the smart grids and integrated energy systems. In recent years, he has published more than 100 peer-reviewed papers and one academic monograph, and he has been authorized over 30 invention patents and participated in the formulation of a national standard. He has hosted 16 national and provincial-level research projects, including the National Excellent Youth Science Foundation and the Regional Joint Key Projects, and he was selected as a top young talent and a "Three-Three-Three" talent in Hebei Province, and awarded the Hebei Youth Science and Technology Nomination Award and the China Invention Association Invention and Entrepreneurship Award. Simultaneously, he is serving as senior members of IEEE, CAA, and CICC, and members of a council of Hebei Association of Automation and Hebei Association of Youth Science and Technology.

Prof. Yanzheng Zhu
Vice Dean of the College of Electrical Engineering and Automation
Shandong University of Science and Technology, China

Speech Title: Research Advances on Bumpless Transfer Control of Switched Systems

Abstract: Switched systems usually require the design of mode-dependent controllers to ensure excellent control performance. However, when the controller accompanies the switching of subsystems, the control input chattering occurs. Severe chattering of control signals may lead to system instability, time delay, mechanical damage, and other problems. Focusing on the issue of control input chattering, this report mainly provides the recent progress in the research on bumpless transfer control of switched systems, including transition-dependent bumpless transfer control methods, bumpless transfer fault-tolerant control methods based on fault reconfiguration, and bumpless transfer control methods for switched affine systems, etc. Finally, some application cases are illustrated to verify the effectiveness of the proposed methods.

Biography: Yanzheng Zhu received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in January 2016. From 2013 to 2015, he was a joint Ph.D. student with the Department of Electrical and Computer Engineering, Ohio State University, Columbus, OH, USA. From 2016 to 2018, he was a Postdoctoral Researcher with the College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China. From 2017 to 2019, he was a Research Fellow with the School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, NSW, Australia. He is currently a Full Professor with the College of Electrical Engineering and Automation, Shandong University of Science and Technology. He has authored or coauthored more than 100 peer-reviewed international journal papers and two monographs in the areas of analysis and control for switched systems. Dr. Zhu serves or has served as an Associate Editor for IEEE Control Systems Letters, Journal of The Franklin Institute, and a Guest Editor for European Journal of Control, etc. He is a recipient of the NSFC for Excellent Young Scholars. His research interests include nondeterministic switched systems, fault diagnosis and tolerant control, and their applications.

Prof. Hongjing Liang
Highly Cited Researchers in Clarivate Analytics
University of Electronic Science and Technology of China, China

Speech Title: Prescribed Performance Cooperative Control Research of Heterogeneous Autonomous Unmanned Systems

Abstract: In recent years, the cooperative control theory of autonomous unmanned systems has been widely applied. For example, through the cooperative operation of heterogeneous autonomous unmanned systems, functions such as information sharing, task allocation, and collaborative strikes can be achieved, thereby improving operational efficiency and capabilities. The various limitations faced by autonomous unmanned systems during task execution, such as communication bandwidth, computing power, energy supply, etc., may affect the performance and cooperative capabilities. Therefore, it is necessary to study how to achieve the prescribed performance cooperative control problem of autonomous unmanned systems. To achieve cooperative control of autonomous unmanned systems with prescribed performance in different scenarios, the consensus tracking control problem of multi-UAV systems under prescribed performance and attitude constraints, and the distributed adaptive cooperative control problem of heterogeneous UAV-UGV systems under prescribed performance are considered. Two types of prescribed performance control strategies operating in different situations are proposed, effectively improving the steady-state and transient performance of systems.

Biography: Hongjing Liang, professor of University of Electronic Science and Technology of China, Doctoral supervisor, Highly Cited Researchers in Clarivate Analytics, NSFC-Excellent Young Scholars, "Tianfu Emei Plan" Young Scientific and Technological Talents Project and Sichuan Natural Science Foundation Outstanding Youth Science Foundation. He has been awarded the title of provincial excellent master's thesis advisor for three consecutive years. At present, his main research direction are intelligent adaptive control of multiagent systems, collective intelligence, etc. He has presided 3 national projects such as National Natural Science Foundation of China projects, and participated in 2 National Natural Science Foundation of China projects. He has published a monograph in English. He is a member of the editorial board of international SCI journals IEEE SMCM, IEEE SMCS, FNL and IJFS. He has published (including accepted papers) more than 100 academic papers in authoritative journals, and the relevant achievements have been cited and positively evaluated by many experts and scholars at home and abroad. One paper was selected for the China's 100 Most Influential International Academic Papers, one paper was selected for the Best Paper Award of Acta Automatica Sinica, and one conference paper was selected for the Best Paper Award in Theory of ICCSS 2017. He was awarded the IEEE SMC Beijing Capital Region Chapte Young Author Prize in 2020.

Prof. Qiang Chen
Director of the Institute of Data-Driven Control and Learning Systems
Zhejiang University of Technology, China

Speech Title: Spatial Repetitive Learning Control for Aperiodic Task Tracking of Rotary Motor Servo Systems

Abstract: Rotary motor servo systems are widely used in many fields such as aerospace, ship propulsion, industrial robotics, etc. In those applications, the rapid and precise control of motor speed is particularly critical to guarantee the overall performance. As is well known, the repetitive learning control (RLC) is an effective control scheme to enable high-precision tracking for the systems performing periodic tasks in the time domain. However, for many aperiodic tasks in time domain, the periodicity exists in the position domain induced by the rotation of the servo motors. It is worth studying how to utilize such position-related spatial periodicity to improve the steady-state tracking accuracy of aperiodic tracking tasks. This report will briefly introduce our preliminary work on the design of spatial repetitive learning control for rotary motor servo systems.

Biography: Qiang Chen earned his Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China, in 2012. Since 2012, He has been with the College of Information Engineering, Zhejiang University of Technology (ZJUT), Hangzhou, China. He is currently a Full Professor and the director of the institute of data-driven control and learning systems in ZJUT. He has presided over 3 National Natural Science Foundation of China (NSFC) projects including National Natural Science Fund for Excellent Young Scholars in 2022, and 1 Key Program of Natural Science Foundation of Zhejiang Province. He has published over 50 academic papers in IEEE Transactions and international journals, and has been authorized more than 60 invention patents, 15 of which were transferred. His research interests include iterative/repetitive learning control and prescribed time control with applications to electromechanical servo systems.

Prof. Keke Huang
Recipient of National High-Level Talent Special Support Programs
Central South University, China
Biography: Keke Huang is a professor and doctoral supervisor at Central South University, serving as the Associate Dean of the School of Automation. He is a recipient of National High-Level Talent Special Support Programs (10,000 Talents Program) for Young Talents, a core member of the national “Huang Dania-style” teaching team and a Distinguished Young Scholar of Hunan Province. He is listed among the top 2% of scientists globally for his long-term research on modeling, optimization decision-making, and coordinated control in the process industry in 2023 and 2024. He has published over 80 high-quality research papers in IEEE Transactions or IFAC journals (including 8 ESI hot papers/highly cited papers), and has been granted over 30 national invention patents. He has led 14 research projects, including key research and development programs of the Ministry of Science and Technology, industrial internet innovation engineering projects of the Ministry of Industry and Information Technology, and general projects of the National Natural Science Foundation of China. He has received the Second Prize of National Science and Technology Progress Award in 2023, the First Prize of Hunan Province Science and Technology Progress Award in 2024, the Excellent Young Scientist Award of the Non-Ferrous Metals Society of China, the First Prize of Science and Technology of the Non-Ferrous Metals Industry of China, and the First Prize of Natural Science of the Chinese Association of Automation. Additionally, he serves as the Deputy Secretary-General of the Automation Branch of the Non-Ferrous Metals Society of China, a committee member of the Process Control Committee and the Fault Diagnosis and Safety Committee of the Chinese Association of Automation.

Prof. Yulong Huang
National Youth Talents
Harbin Engineering University, China
Biography: Yulong Huang, Professor, PhD Supervisor, National Youth Talents. He has long been devoted to intelligent information fusion theory and intelligent navigation application research. He has hosted more than 10 national, provincial and ministerial projects, including the key projects of the National Natural Science Foundation of China, National key research and development program, and the key projects of the Frontier Center of the Ministry of Education. He has published more than 60 top IEEE transactions papers as the first author or corresponding author. He has been selected into the list of the world's top 2% scientists, The Youth Talent Support Program of the China Association for Science and Technology , the Xiangjiang Scholars Program, and the upward and good youth of Heilongjiang Province, and won the first prize of the Heilongjiang Natural Science Award, the first prize of the Natural Science Award of the Chinese Society of Automation, the Wu Wenjun Artificial Intelligence Outstanding Youth Award, the IEEE Barry Carlton Award, the IEEE Barry Carlton Award Honorable Mention, and the Outstanding Doctoral Dissertation Award of the Chinese Society of Automation. He is currently the associate editor of IEEE Transactions on Automatic Control, IEEE Transactions on Automation Science and Engineering, IEEE Transactions Aerospace and Electronic Systems, IEEE Transactions on Instrumentation and Measurement and IEEE Sensors Journal.

Prof. Long Jin
Recipient of National-Level Youth Talent Program
Lanzhou University, China

Speech Title: Modeling and Analysis of the Matthew Effect in Social Networks

Abstract: The Matthew effect is prevalent in social networks, economic systems, and scientific collaboration, characterized by the unequal accumulation of resources, often encapsulated by the saying, ``the rich get richer, and the poor get poorer.’’ In real-world societies, individual interactions are typically distributed, with decisions and behaviors primarily relying on direct communication with neighbors. Consequently, distributed modeling not only provides a closer representation of real-world environments but also helps to reveal how local competition influences the evolution of global resource distribution. This paper presents a modeling framework for the Matthew effect based on distributed k-winners-take-all (kWTA) networks aimed at capturing competitive resource allocation and accumulation among individuals in society.

Biography: Long Jin is a professor at the School of Information Science and Engineering at Lanzhou University. He has been selected for the ``National Young Talents Program’’, the Gansu Provincial Leading Talent Program. He has led multiple projects, including four National Natural Science Foundation of China (NSFC) projects, one key project under the National Key R&D Program, and several key/excellent youth projects from the Gansu Provincial Natural Science Foundation. Over the past five years, as the first or corresponding author, he has published more than 100 high-level papers in IEEE Transactions journals, Automatica, and CCF A-class journals and has been granted 24 invention patents. Since 2020, he has been continuously included in Elsevier's ``China Highly Cited Researchers.’’ He has received several awards, including the Gansu Provincial Natural Science Second Prize, the China Association of Automation Natural Science Second Prize, and the Wu Wenjun Artificial Intelligence Excellent Young Scholar Award. He currently serves as an associate editor for several journals, including IEEE TIE, TIV, IEEE/CAA JAS, and Neural Networks.

Assoc. Prof. Hongtian Chen
Shanghai Jiao Tong University, China

Speech Title: Explainable Fault Diagnosis: A Bridge Between Unsupervised and Supervised Learning-based Fault Diagnosis Approaches

Abstract: The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More in detail, we parameterize nonlinear systems through a generalized kernel representation used for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance by the use of this bridge. In order to have a better understanding of the results obtained, unsupervised and supervised neural networks are chosen as the learning tools to identify generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This report is a perspective talk, whose contribution lies in proposing and detailing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.

Biography: Hongtian Chen received his Ph.D. degree in College of Automation Engineering from Nanjing University of Aeronautics and Astronautics, China, in 2019. He was a Post-Doctoral Fellow at the University of Alberta, Canada, from 2019 to 2023. Dr. Chen joined Shanghai Jiao Tong University, China in 2023 as an Associate Professor in the Department of Automation. His research interests include fault diagnosis and fault-tolerant control, data mining and analytics, machine learning, and cooperative control; and their applications in high-speed trains, new energy systems, industrial processes, and autonomous systems. Dr. Chen was a recipient of the Grand Prize of Innovation Award of Ministry of Industry and Information Technology of the People's Republic of China in 2019, the Excellent Ph.D. Thesis Award of Jiangsu Province in 2020, the Excellent Doctoral Dissertation Award from Chinese Association of Automation (CAA) in 2020, and Marie Skłodowska-Curie Actions in 2023.

Assoc. Prof. Mingming Zhang
Senior Member, IEEE
Southern University of Science and Technology, China

Speech Title: Task-oriented brain-robot interaction technology for medical rehabilitation

Abstract: Rehabilitation robotics has emerged as a global research priority, addressing the needs of one-third of the world's population requiring rehabilitation services. While these technologies partially mitigate therapist shortages, their evidence-based clinical benefits remain limited, as noted in Science Robotics (2021). Current systems face three key challenges: (1) restricted task simulation to kinematic forms, (2) difficulty balancing large workspace with high-precision tactile feedback, and (3) insufficient personalized, task-matched interaction. This talk presents advancements in upper-limb daily task and lower-limb walking rehabilitation robots, along with novel neural feedback approaches, to bridge these critical gaps and enhance clinical effectiveness.

Biography: Dr. Mingming Zhang is a tenured Associate Professor and PhD supervisor in the Department of Biomedical Engineering at the Southern University of Science and Technology. He has been recognized as a National High-Level Young Talent, Young Scientist of the National Key R&D Program, and recipient of the Guangdong Outstanding Young Scholars Fund. Currently serving as Director of the Brain-Robot Rehabilitation Technology Laboratory, Dr. Zhang specializes in innovative theories and key technologies in rehabilitation robotics and brain-computer interaction. His developed systems - including the task-oriented cane-assisted exoskeleton system, task-oriented tactile feedback system, and task-oriented intention decoding system - have been successfully implemented in rehabilitation training for hundreds of patients. His research findings have been published in over 100 papers appearing in prestigious international journals and conferences including Nature Communications, IEEE Transactions series (TFS, TII, TIE, T-Mech, TASE, TIM, TNSRE, etc.). Dr. Zhang has received several honors including the Science and Technology Award from the Chinese Association of Rehabilitation Medicine, Natural Science Award from the AI & Automation Association of Guangdong-Hong Kong-Macao Greater Bay Area, and Robotics Science and Technology Award of Guangdong Province. He currently serves as Associate Editor for multiple high-impact journals including IEEE TASE, IEEE TNSRE, IEEE RAL, and IEEE TMRB.