Biography / 个人简介:
Prof. Huaicheng Yan, East China University of Science and Technology, China
Speech Title: Event-triggered Control and Applications of Nonlinear Networked Control Systems
Abstract: This talk will introduce some basic concepts and the latest research progress of networked control systems and event triggered control. Based on the fuzzy control methods, the latest research results of event triggered control and filtering of networked Markov jump systems, multi-agent systems and sampled-data control systems based on limited information transmission are introduced respectively, and their stability analysis and intelligent controller design methods are also given. Finally, some verifications of examples are given and some relevant practical applications are also be presented.
Biography: Prof. Huaicheng Yan received his B.Sc. degree in automatic control from Wuhan University of Technology, China, in 2001, and the Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology, China, in 2007. From 2007 to 2009, he was a Postdoctoral Fellow with the Chinese University of Hong Kong. Currently, he is a Professor with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. He has published more than 200 SCI-indexed papers, including more than 130 papers published in top journal of Automatica and IEEE Transactions journals. He is the Highly Cited Researcher of Clarivate from 2019 to 2022. He is an associate editor for IEEE Transactions on Neural Networks and Learning Systems, International Journal of Robotics and Automation and IEEE Open Journal of Circuits and Systems, etc. His research interests include networked control systems, multi-agent systems and robotics.
Prof. Qinmin Yang, Zhejiang University, China
Speech Title: Enhancing Wind Energy Harvesting by Industrial Data Intelligence
Abstract: Wind energy has been considered to be a promising alternative to current fossil-based energies. Large-scale wind turbines have been widely deployed to substantiate the renewable energy strategy of various countries. In this talk, challenges faced by academic and industrial communities for high reliable and efficient exploitation of wind energy are discussed. Industrial data intelligence is introduced to (partially) overcome problems, such as uncertainty, intermittence, and intense dynamics. Theoretical results and attempts for practice are both present.
Biography: Qinmin Yang received the Bachelor's degree in Electrical Engineering from Civil Aviation University of China, Tianjin, China in 2001, the Master of Science Degree in Control Science and Engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 2004, and the Ph.D. degree in Electrical Engineering from the University of Missouri-Rolla, MO USA, in 2007.
From 2007 to 2008, he was a Post-doctoral Research Associate at University of Missouri-Rolla. From 2008 to 2009, he was an advanced system engineer with Caterpillar Inc. From 2009 to 2010, he was a Post-doctoral Research Associate at University of Connecticut. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. He has also held visiting positions in University of Toronto and Lehigh University. He has been serving as an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Neural Networks and Learning Systems, Transactions of the Institute of Measurement and Control, Processes, and Automatica Sinica. His research interests include intelligent control, renewable energy systems, smart grid, and industrial big data.
Prof. Qiang Liu, Northeastern University, China
Speech Title: Working Condition Identification in the Era of Big Data and Industrial Internet
Abstract: Intelligent manufacturing of modern industrial processes is the solution towards safe and efficient, green operation. The working condition identification is a major concern of intelligent manufacturing in the era of big data and industrial internet. This talk will first review the existing work on working condition identification. In view of the spatiotemporal characteristics of the image sequences of the furnace shell under abnormal conditions, and the existence of strong disturbances caused by water mist, etc, a novel deep learning architecture for operation condition identification with robustness to disturbances is presented. The experimental results on a real fused magnisum furnace demonstrate the effectiveness of the proposed method. Finally, the future development on working condition identification is prospected.
Biography: Qiang LIU, Professor at State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, China. He is a senior member of IEEE and the recipient of the Outstanding Young Scholar of Liaoning Revitalization Talents Program, China. He is the Committee Member and Secretariat-General of the Technical Committee on Big Data of the Chinese Association of Automation. He is also the Committee Member of the IFAC Technical Committee on Fault Detection, Supervision and Safety for Technical Processes. His research interests include big data analytics, machine learning, statistical process monitoring and fault diagnosis of complex industrial processes. He has published more than 60 peer-reviewed papers. He won a number of academic awards, including the Best Paper Award of 2018 IEEE Conference on Intelligent Rail Transportation . He is a principal investigator of two Key projects supported by Natural Science Foundation of China and National Key Research and Development Program of China. He is the Editor/Guest Editor of a few International Journals, including the Associate Editor of Intelligence & Robotics, and head Guest Editor of a special issue on "Advanced Intelligent Manufacturing System: Theory, Algorithms, and Industrial Applications" in the IEEE Transactions on Industrial Informatics.
Prof. Dong Wang, Dalian University of Technology, China
Speech Title: Distributed Optimization based on multiagent systems
Abstract: In the past years, distributed optimization based on multiagent systems has received considerable attention due to its wide applications in smart grid, resource allocation and machine learning. Common features of these examples are that there is no centralized center involved and the resources, such as sensing, communication and computation, are usually connected by the networks and communicate in the local networks, which is called distributed algorithms. In this talk, I will first give some concepts related to distributed optimization and communication model. Then, I will introduce several recent works on distributed optimization. Application to economic dispatch problems in smart grids will also be discussed.
Biography: Dong Wang received the B.Sc. degree in automation and the M. Eng. degree in control theory and control engineering from the Shenyang University of Technology, Shenyang, China, in 2003 and 2006, respectively, and Ph.D. degree in control theory and control engineering from the Dalian University of Technology, China, in 2010. Since 2010, he has been with the Dalian University of Technology, where he is currently a professor with the School of Control Science and Engineering. His current research interests include multiagent systems, distributed optimization, fault detection and switched systems.
Dr. Wang is Associate Editors of Information Sciences and Neurocomputing.
Prof. Xiangpeng Xie, Nanjing University of Posts and Telecommunications, China
Speech Title: Advanced fuzzy manufacturing process control system and safety protection for cold rolling
Abstract: "Internet plus" promotes the transformation of manufacturing mode, and intelligent manufacturing becomes a new mode of production. Cold rolling process control system, as a neck-sticking technology in advanced manufacturing industry, contains some common scientific and technological problems in the industry field. This speech is oriented to the transformation and upgrading needs of the cold rolling industry, gives full play to the characteristics of control science and engineering disciplines, and the data-driven advanced manufacturing process control system and safety protection of cold rolling are studied. That is, the mechanism-data joint modeling method integrated with the key information upstream of rolling mill is proposed to solve complex mechanism the heterogeneous data modeling problem affected by coupling of cold rolling; Change from empirical judgment mode to data-supported man-machine cooperative decision-making judgment mode to realize seamless connection between personalized requirements and underlying control instructions; Based on the idea of periodic physical examination of equipment parameters and early warning active protection, a fuzzy expert system which can ensure the safe operation of key equipment is constructed. In view of the above theoretical research, rolling data acquisition and intelligent control system are carried out through technical cooperation with well-known cold rolling enterprises for technical verification.
Biography: Prof. Xiangpeng Xie received the B.S. degree and Ph.D. degree in engineering from Northeastern University, Shenyang, China, in 2004 and 2010, respectively. He is currently a full professor with the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China. He is the winner of National Natural Science Foundation of Excellent Youth Foundation (2020) and Jiangsu Province Science Foundation for Distinguished Young Scholars (2019). He has been identified as the ESI Highly Cited Researchers from Clarivate (2020 and 2021).
Prof. Youqing Wang, Beijing University of Chemical Technology, China
Speech Title: State Monitoring of Industrial Processes Based on Multivariate Statistical Methods
Abstract: Multivariate statistical state monitoring methods are significant for industrial processes to improve their production efficiency and enhancing safety. Two methods will be introduced in this presentation: orthonormal subspace analysis (OSA) and recursive correlative statistical analysis (RCSA ). OSA can divide process data and key performance indicators (KPI) data into three orthonormal subspaces through an analytic solution. Hence, OSA not only can detect a fault but also can judge whether the fault is KPI-related or KPI-unrelated. RCSA is a powerful tool to detect incipient faults and has been proved to have less calculation complexity compared with standard method. Finally, an industrial state monitoring system will be introduced, which was designed by our group.
Biography: Youqing Wang received the B.S. degree in Mathematics from Shandong University, Jinan, Shandong, China, in 2003, and PhD degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2008. He worked chronologically at Hong Kong University of Science and Technology, Hong Kong, China; University of California, Santa Barbara, USA; University of Alberta, Edmonton, Canada; Shandong University of Science and Technology, Qingdao, China; City University of Hong Kong, Hong Kong, China. He is currently a professor and the dean of College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. His research interests include fault-tolerant control, state monitoring, iterative learning control, and their applications on chemical processes. Dr. Wang was a recipient of several research awards, including The National Science Fund for Distinguished Young Scholars, IET Fellow, Journal of Process Control Survey Paper Prize, and ADCHEM2015 Young Author Prize.
Prof. Hao Luo, Harbin Institute of Technology, China
Speech Title: Subspace-aided Closed-loop Robust Fault Detection for Automatic Control Systems
Abstract: This talk reports a robust subspace fault monitoring method for industrial automatic control systems with unknown disturbances. The novelty of the given approach relies on the closed-loop data-driven realization of the stable kernel representation of the automatic control systems. In order to ensure accurate and robust closed-loop identification, the method first analyzes the mapping between the closed-loop measurement data and the unknown disturbance, and determines the kernel space of the automatic control system which is decoupled from the unknown disturbance, and then derives a robust data-driven fault detection approach, which leads to an accurate detection based on the evaluation of disturbance-decoupled residual signals.
Biography: Prof. Hao Luo received his B.E. degree in electrical engineering from Xi’An Jiaotong University, China, in 2007, M.Sc. degree in electrical engineering and information technology from University of Duisburg-Essen, Germany, in 2012, and the Ph.D. degree at the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany, in 2016. He is currently a full professor in School of Astronautics, Harbin Institute of Technology, China. His research interests include model-based and data-driven intelligent process monitoring and performance optimization, as well as their plug-and-play applications to industrial control systems. He currently serves as Associate Editor of IEEE Trans. on Artificial Intelligence and IEEE Access, as well as Guest Editor of IEEE Trans. on Industrial Informatics.
Assoc. Prof. Ziyang Meng, Tsinghua University, China
Speech Title: Intelligent Navigation Technology
Abstract: Artificial intelligence technology promotes the development and application of unmanned systems. Autonomous navigation capability is the most basic capability of unmanned systems. Traditional navigation methods mainly use GNSS or inertial units, which are subject to interference and rapid divergence. Environment perception and applying artificial intelligence technology is a current research hotspot to improve the autonomous navigation capability of unmanned systems. This talk specifically reports the recent research progress of our research group in intelligent navigation technology. The contents mainly includes: remote sensing image matching and positioning technology based on convolutional neural network, simultaneous positioning and mapping method in complex environment, simultaneous positioning and mapping technology based on hardware acceleration, and "end-to-end" vision-based navigation under ultra-low power platform. It is expected to provide more accurate, faster and more robust navigation solutions for different unmanned system platforms.
Biography: Ziyang Meng is currently an associate professor with the Department of Precision Instrument, Tsinghua University, China. He received his B.S. degree with honors from Huazhong University of Science \& Technology, Wuhan, China, in 2006, and Ph.D. degree from Tsinghua University, Beijing, China, in 2010. He was an exchange Ph.D. student at Utah State University, Logan, USA from 2008 to 2009. Prior to joining Tsinghua University, he held postdoc, researcher, and Humboldt research fellow positions at, respectively, Shanghai Jiao Tong University, Shanghai, China, KTH Royal Institute of Technology, Stockholm, Sweden, and Technical University of Munich, Munich, Germany from 2010-2015. His research interests include distributed control and optimization, space science, and intelligent navigation technique. He serves as associate editors for Systems \& Control Letters and IET Control Theory \& Applications. He is a Senior Member of IEEE and a Fellow of IET.
Assoc. Prof. Zhiwen Chen, Central South University, China
Speech Title: Canonical correlation analysis-based fault diagnosis method and its applications
Abstract: In order to meet the demands for high product quality, efficient operation and overall system reliability, the complexity and automation of modern industrial processes have significantly increased. This development brings great challenge in handling abnormal situation, such as performance degradation and component faults. Canonical correlation analysis (CCA) is a typical multivariate analysis tool that is now widely used for process monitoring and fault diagnosis. In this talk, canonical correlation analysis (CCA)-based process monitoring and fault diagnosis methods, which are developed to deal with various characteristics of industrial processes, will be focused and detailed. Furthermore, its applications will also be introduced.
Biography: Zhiwen Chen received his M.S.degree in electronic information and technology from Central South University, China in 2012, Ph.D.degree in electrical engineering and information technology from University of Duisburg-Essen, Germany in 2016. He is currently an associate professor at School of Automation, Central South University. He has published more than 60 peer-reviewed journal/conference papers, one book, one book chapter, and hold 13 patents. He is serving on, or has served on, several international conference as Program or Session Chair/co-chair. His research interests are fault diagnosis, process monitoring, prognostic and health management, system identification, machine learning and data mining, graph neural network.
Dr. Shang Chao, Tsinghua University, China
Speech Title: Data-Driven Control of Stochastic Systems: An Active Innovation Learning Paradigm
Abstract: Recent years have witnessed a booming interest in the data-driven paradigm for predictive control. However, under noisy data ill-conditioned solutions could occur, causing inaccurate predictions and unexpected control behaviors. In this article, we explore a new route toward data-driven control of stochastic systems through active offline learning of innovation data, which gives an answer to the critical question of how to derive an optimal data-driven model from a noise-corrupted dataset. A generalization of the Willems' fundamental lemma is developed for non-parametric representation of input-output-innovation trajectories, provided realizations of innovation are precisely known. This yields a model-agnostic unbiased output predictor and paves the way for data-driven receding horizon control, whose behavior is identical to the “oracle" solution of certainty-equivalent model-based control with measurable states. For efficient innovation estimation, a new low-rank subspace identification algorithm is developed. Numerical studies show that by actively learning innovation from input-output data, remarkable improvement can be made over popular empirical regularizations, thereby offering a promising framework for data-driven control of stochastic systems.
Biography: Chao Shang received the B.Eng. degree in automation and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2011 and 2016, respectively. After working as a Postdoctoral Fellow at Cornell University, he joined the Department of Automation, Tsinghua University in 2018, where he is currently an associate professor. His research interests include data-driven modeling, monitoring, diagnosis and optimization with applications to industrial manufacturing processes. Dr. Shang is the recipient of Springer Excellent Doctorate Theses Award, Emerging Leaders in Control Engineering Practice, Best Paper Award of 1st International Conference on Industrial Artificial Intelligence, Zijing Scholarship of Tsinghua University, among others.