Keynote Speakers
Prof. Bin Zhou Harbin Institute of Technology, China Zhou Bin is a recipient of the National Outstanding Youth Science Fund; an awardee of the National Hundred Excellent Doctoral Dissertations; a recipient of the Chinese Youth Science and Technology Innovation Award; a recipient of the Excellent Young Scientists Fund from the National Natural Science Foundation; a winner of the Second Prize of the National Natural Science Award; a selected member of the Education Ministry's New Century Excellent Talents Support Program; and a recipient of the First Prize of the Natural Science Award from the Chinese Association of Automation (ranking first). He is a professor and doctoral supervisor at the Harbin Institute of Technology, and the director of the Control Theory and Guidance Technology Research Center. His research interests include time-varying systems, nonlinear control, multi-agent systems, spacecraft control, and aircraft guidance and control. He has published over 200 papers in top journals in the field, such as Automatica, IEEE Transactions on Automatic Control, and SIAM Journal on Control and Optimization. He currently serves on the editorial boards of international journals including Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Aerospace and Electronic Systems, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. Speech Title: Prescribed-Time Control by Periodic Delayed Feedback Abstract: Prescribed-time control has been a hot topic in recent years. Existing prescribed-time control methods are mainly based on time-varying high-gain feedback (THF). However, since an infinite gain is practically impossible to achieve, such kind of controllers are not well-defined at and after the prescribed settling time. Recently, another control scheme capable of achieving prescribed-time stabilization, namely, a periodic delayed feedback (PDF) scheme, was proposed. An appealing feature of the PDF is that the time-varying controller gain is bounded and can be chosen continuous, continually differentiable and even smooth, thereby avoiding the inherent drawbacks of THF. Over the past three years, there have been many interesting and significant developments in the PDF scheme, achieving prescribed-time stabilization of linear delay systems, strict feedback systems and single input normal nonlinear systems. As applications of the proposed methods, the prescribed-time control problems of spacecraft rendezvous control systems, hypersonic vehicle systems and manipulator systems have been investigated. Numerical simulations show the effectiveness of the proposed methods. |  |
Prof. Guangchen Wang Shandong University, China Guangchen Wang received the B.S. degree in mathematics from Shandong Normal University in 2001, and the Ph.D. degree in probability theory and mathematical statistics from the School of Mathematics and System Sciences, Shandong University in 2007. From July 2007 to August 2010, he served as a Lecturer at the School of Mathematical Sciences, Shandong Normal University. He joined the School of Control Science and Engineering, Shandong University as an Associate Professor in September 2010, where he has been a Full Professor since September 2014. His research interests include stochastic control, nonlinear filtering, and mathematical finance. He is the Recipient of Outstanding Young Project of the National Natural Science Fund. Speech Title: Robust optimal control of Bi-objective LQ system with noisy observation Abstract: This talk is concerned with a kind of partially observable LQ control problem, where the coefficients of cost functional are uncertain representing different market conditions. By virtue of backward separation technique, stochastic maximum principle, as well as filtering method, a feedback form of candidate optimal control is designed. Moreover, through some delicate analysis, the existence of maximal reference probability is certified. Finally, a numerical simulation is presented to authenticate the theoretical results. |  |
Prof. Xiaowu Mu Zhengzhou University, China Xiaowu. Mu, a distinguished professor and doctoral supervisor at Zhengzhou University, received his Bachelor's, Master's, and Doctoral degrees from Peking University in 1983, 1988, and 1991, respectively. Since 1993, he has enjoyed the State Council Special Allowance for Outstanding Contributions. From 2006 to 2017, he served as a member of the Undergraduate Teaching Guidance Committee of Education Department. Since 2021, he has served as the Executive Director and Vice Chairman of Henan Mathematical Society. He was selected for the 2024 edition of the World’s Top 2% Scientists list. His research primarily focuses on Nonlinear Systems, Stochastic Hybrid Systems, Networked Systems, and Multi-Agent Systems. He has led four projects supported by the National Natural Science Foundation of China and published over 180 high-level academic papers. Speech Title: Event-Triggered Tracking Control for Networked Random Switched System under Constrained Environments Abstract: With the continuous advancement of computer technology and network communication technology, networked systems have garnered sustained attention from scholars due to their advantages. In practical control scenarios, networked systems are often constrained by various factors. This report focuses on the tracking control problem of networked random switched systems under complex constraint environments such as control execution constraints, network security limitations, system mode transmission restrictions, and communication resource scarcity. Based on the hidden (semi-) Markov model and memory-based event-triggered mechanism, an asynchronous tracking controller is designed to guarantee the stochastic stability and extended dissipative performance of tracking error system, and effectively reduce the transmission of redundant data. |  |
Prof. Derui Ding University of Shanghai for Science and Technology, China Derui Ding received the Ph.D. degree in Control Theory and Control Engineering in 2014 from Donghua University, Shanghai, China. He is currently aprofessor in University of Shanghai for Science and Technology, Shanghai, China. His research interests include distributed control, filtering and optimization, learning-based control as well as data mining. He was a Highly Cited Researcher in 2019 and 2020 according to Clarivate Analytics. He was the recipient of the 2021 Nobert Wiener Review Award from IEEE/CAA Journal of Automatica Sinica, and the 2020 and 2022 Andrew P. Sage Best Transactions Paper Awards from the IEEE Systems, Man, and Cybernetics (SMC) Society. He is a Senior Member of both the Chinese Association of Automation (CAA) and the Institute of Electrical and Electronic Engineers (IEEE). He is serving as a Deputy Editors-in-Chief for International Journal of Network Dynamics and Intelligence, an Associate Editor for IEEE Transactions on Industrial Informatics, IEEE/CAA Journal of Automatica Sinica, Neurocomputing and IET Control Theory & Applications. Speech Title: Secure Platooning Control of Automated Vehicles Abstract: Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This talk focuses on the distributed secure control issue of automated vehicles under replay attacks or privacy-preserving. A proportional integral observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Considering replay attacks, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. For privacy-preserving, sampled-data-based dynamic encryption and decryption schemes, featuring a dynamic private key, are developed such that the encrypted vehicle-to-vehicle data can be kept private to each platoon vehicle. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control strategy. |  |
Dr. Weibo Liu Brunel University of London, UK Weibo Liu received the B.S. degree in electrical engineering from the Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, U.K., in 2015, and the Ph.D. degree in artificial intelligence in 2020 from the Department of Computer Science, Brunel University London, Uxbridge, U.K. He is currently a Lecturer in the Department of Computer Science, Brunel University London, Uxbridge, U.K. His research interests include intelligent data analysis, evolutionary computation, transfer learning, machine learning and deep learning. He was the recipient of the 2019 Chinese Government Award for Outstanding Self-financed Students Abroad. He is a Member of the Institute of Electrical and Electronic Engineers (IEEE). He serves as an Associate Editor for the Journal of Ambient Intelligence and Humanized Computing and the Journal of Cognitive Computation. He is a very active reviewer for many international journals and conferences. Speech Title: AI-Based Industrial Data Analytics for Metal Additive Manufacturing Abstract: Metal Additive manufacturing (MAM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the MAM process, it is of critical importance to carry out data analytics on MAM process by monitoring the sensor data collected and detecting potential defects/outliers. This goal of data analytics leads to the development of a knowledge-based system which is to readapt Product engineering stages: 1) Building an AI model to detect future deviations caused by complex geometries to propose alternative geometry changes; and 2) Modifying the manufacturing strategy based on trained AI algorithm to avoid deposition paths that cause final distortions or heat accumulation. In this talk, we focus on the defect detection of thermal image data and outlier detection of welding sensor data based on artificial intelligence techniques. In the first part, a novel image processing method, an image-enhancement generative adversarial network, with aim to improve the contrast ratio of the thermal images for image segmentation will be discussed. In the second part, a novel clustering-based outlier detection method for anomaly detection will be introduced. The proposed methods are exploited in analyzing the real-world industrial data collected from a wire arc MAM pilot line in Sweden. |  |
Prof. Raquel Caballero Águila University of Jaén, Spain Raquel Caballero-Águila is a Professor in the Department of Statistics and Operations Research at the University of Jaén, Spain. She received her MSc and PhD degrees in Mathematics from the University of Granada, Spain, in 1997 and 1999, respectively. Her research interests include time-varying stochastic systems, complex networks and design of estimation al algorithms to address emerging challenges in networked systems. Prof. Caballero-Águila has authored numerousscientific papers in refereed international journals indexed in Journal Citation Reports (JCR) and has been included in recent editions of the Spanish National Research Council (CSIC) ranking of most cited Spanish female researchers. She has participated in different research projects both as a research member and as a principal investigator. She serves as a reviewer for Mathematical Reviews and is an academic editor for the journals Mathematical Problems in Engineering, Journal of Control Science and Engineering, and Systems Science and Control Engineering. In addition, she has reviewed for prestigious international journals, and has actively contributed to the organization and chairing of sessions at international conferences. Her research has been further enriched through international collaborations, including research visits to Kagoshima University (Japan) and Harbin University of Science and Technology (China). Speech Title: Distributed estimation in networked stochastic systems. Some recent advances and perspectives Abstract: Distributed estimation in sensor networks plays a fundamental role in modern system design, enabling efficient monitoring and control across various applications, from target tracking to industrial automation. Unlike centralized approaches, where all sensor data are transmitted to a central unit for processing, distributed estimation allows each sensor node to locally process its information while exchanging data only with neighbouring nodes according to the network topology. This decentralized strategy reduces communication overhead and minimizes energy consumption, something essential in resource-constrained networked systems. In this keynote, we will first provide an overview of distributed estimation, emphasizing its theoretical and practical significance while highlighting recent advancements in the field. We will then discuss key complexities arising in real-world networked systems, focusing on four major challenges: random parameter variations, measurement quantization, time-correlated noise, and mixed random attacks. Recent research efforts addressing these challenges will be presented, followed by a discussion on future research directions and open problems. Finally, some key references will be provided to support further studies in this area. |
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Dr. Chaoqing Jia Harbin University of Science and Technology, China Chaoqing Jia is an Associate Professor and Master Supervisor in the Department of Artificial Intelligence, Harbin University of Science and Technology, Harbin, China.Hereceived the Ph.D. degree in Operational Research and Cybernetics from the Department ofApplied Mathematics, Harbin University of Science and Technology, Harbin, China, in 2022.FromFebruary 2024 toFebruary 2025, he was an academic visitor with the Department of Computer Science, Brunel University London, Uxbridge, U.K. He has published over 20 SCI indexed papers, authored 2 monographs, and granted 8 invention patents. Moreover, he has hosted 6 projects under research including National Natural Science Foundation of China, Heilongjiang Provincial Natural Science Foundation of China, Heilongjiang Provincial Postdoctoral Science Foundation of China, etc.He isanIEEE Member andactive reviewer for many international journals and conferences. His research interests include distributed filtering and optimal state estimation for complex dynamical systems. Speech Title: Recursive State Estimation and Boundedness Analyses for Complex Dynamical Networks under Unreliable Communication Abstract: Complex dynamical networks have already gained widespread research attention due to their applications in a variety of fields such as ecological networks, social networks, traffic networks, smart grids, and so on. Notably, the design of state estimation algorithms is of great significance to understand the dynamical behavior of complex networks. In addition, the phenomena of unreliable communication including probability-type quantification and transmission strategies are considered in the design of state estimation algorithm for complex networks with varying topologies, characterizing the unreliability or insecurity of networked environment. In this talk, the variance-constrained state estimation algorithms are developed recursively and some sufficient conditions are elaborated to guarantee that the estimation errors are bounded. Finally, the simulation results are given to illustrate the effectiveness and feasibility of our developed unreliable-communication-based state estimation scheme. |  |
