Development Forum 3: Modeling, Optimization and Control of Complex Systems

Panelists:

Lei Guo（Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China）

Jie Chen（Harbin Institute of Technology, China）

Hyungbo Shim（Seoul National University, Korea）

Long Cheng（Institute of Automation, Chinese Academy of Sciences, China）

Youqing Wang（Beijing University of Chemical Technology, China）

Zhixin Liu （Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China）

Jianqiang Li（Shenzhen University, China）

Abstract: The purpose of this forum is both to commemorate the 40th (20th for the English edition) anniversary of the journal and to provide an opportunity to discuss current and future developments in systems and control. Speakers at the forum include internationally renowned scholars and well-established younger researchers. They will present their latest research and give their views on new directions in fields ranging from MPC, autonomous systems, encrypted control to multi-agent systems, robot skill learning and clustering analysis in machine learning.

Speaker: Lei Guo, Academy of Mathematics and Systems Science, Chinese Academy of Sciences

Title: Convergence of adaptive MPC

Abstract: We will discuss the convergence of an adaptive model predictive control (MPC) algorithm for discrete-time linear stochastic systems with unknown parameters. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of online estimated models, which are built on the weighted least-squares (WLS) estimates modified by both the random regularization and attenuating excitation methods introduced earlier in stochastic adaptive control. By using the Markov chain ergodic theory, it is shown that the adaptive MPC performance will converge asymptotically to the ergodic MPC performance with known parameters.

Lei Guo is a professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS).  In 2019, he was awarded the Hendrik W. Bode Lecture Prize by the IEEE Control Systems Society "for fundamental and practical contributions to the field of adaptive control, system identification, adaptive signal processing, stochastic systems, and applied mathematics". His current research interests include adaptive (learning, filtering, control and games) theory of stochastic systems, control of uncertain nonlinear systems, learning based-intelligent control systems, game-based control systems, multi-agent complex systems, judicial sentencing, and man-machine integration systems, etc.

Speaker:  Jie Chen, Harbin Institute of Technology

Title: Autonomous Semantic Collaboration and Control of Novel Unmanned Platforms

Abstract: This report aims to typical disaster relief scenarios, building autonomous semantic collaboration capabilities for unmanned platforms. Centered on a system architecture of perception–decision–planning–control–hardware support, it leverages semantic information (e.g., natural language descriptions of location, features, and objects) as a bridge between human commanders and unmanned swarms. Key technical breakthroughs include global hierarchical semantic map generation, autonomous parsing of natural language tasks, semantic navigation with reactive replanning, multi-platform cooperative control, and ergodic exploration. On the hardware level, a tensegrity-based unmanned platform with both compliance and structural stability is designed to ensure strong environmental adaptability and dynamic responsiveness. The integrated system significantly enhances the platform’s intelligent understanding and collaborative control in post-disaster scenarios, providing solid support for real-world deployment and future research.

Jie Chen, Professor of Harbin Institute of Technology (HIT), Member of the Chinese Academy of Engineering, IEEE Fellow, IFAC Fellow. He currently serves as the Director of the National Key Laboratory of Autonomous Intelligent Unmanned Systems (also Director of Frontier Science Center for the Ministry of Education and the Shanghai Research Institute for Autonomous Intelligent Unmanned Systems). He is the Principal Investigator of the National Natural Science Foundation of China (NSFC) Basic Science Center Project and Innovation Research Group Project, and holds editorial roles including editor-in-chief, associate editor, and editorial board member for several leading academic journals.

His main research interests include multi-objective optimization and control of complex systems, as well as cooperative control of multi-agent systems. He has proposed and developed theoretical frameworks and methodologies for hybrid intelligent optimization and stability in distributed cooperative control, making key breakthroughs in the distributed coordination of multiple mobile platforms. In recent years, as the first contributor, he has received one Second Prize of the State Natural Science Award, two Second Prizes of the State Scientific and Technological Progress Award, four First Prizes at the provincial and ministerial level, and the Ho Leung Ho Lee Foundation Award for Scientific and Technological Progress. He has published over 100 SCI-indexed papers, holds more than 40 authorized invention patents as the first inventor, and is the author of five academic monographs, one textbook, and one translated work.

Speaker: Hyungbo Shim, Seoul National University

Title: An Introduction to Encrypted Controller

Abstract: In order to protect feedback control systems, cryptography is often employed, and the communicated signals are encrypted. However, for the controller to perform arithmetic operations, the encrypted sensor signals must be decrypted, which increases the system's vulnerability to attackers. Fortunately, this is not the case when employing homomorphic encryption (HE) techniques. This talk briefly introduces the Learning With Errors (LWE) method, one of the HE techniques, and discusses the challenges and solutions associated with using HE in feedback control systems.

This presentation will systematically introduce novel approaches in complex systems, including pinning cooperative control theory for complex networks, consensus control in dynamical systems, coordinated control under network bandwidth constraints and safety-critical limitations, and intelligent sensing technologies. Its applications in engineering will be highlighted. Finally, emerging trends in cooperative control and intelligent perception will be discussed.

Hyungbo Shim received the B.S., M.S., and Ph.D. degrees from Seoul National University, Korea, and held the post-doc position at University of California, Santa Barbara till 2001. He joined Hanyang University, Seoul, in 2002. Since 2003, he has been with Seoul National University, Korea. He served as associate editor for Automatica, IEEE Trans. on Automatic Control, Int. Journal of Robust and Nonlinear Control, and European Journal of Control, and as editor for Int. Journal of Control, Automation, and Systems. He serves for the IFAC World Congress 2026 as the general chair. He is ICROS fellow, IFAC Distinguished Lecturer, and a member of the Korean Academy of Science and Technology. His research interests include stability analysis of nonlinear systems, observer design, disturbance observer technique, secure control systems, and synchronization for multi-agent systems.

Speaker: Long Cheng, Institute of Automation, Chinese Academy of Sciences

Title: Robotic Skill Learning Based on the Dynamic System Approach

Abstract: In the era of rapid advancements in intelligent technologies, robot skill learning has become a key approach to enhance autonomous adaptability and a critical direction for advancing intelligent systems. This report focuses on dynamic system-based skill learning, systematically tracing its evolution from foundational theories to recent progress. The report also discusses innovative methods like cost function learning and their role in improving skill acquisition frameworks. By analyzing practical use cases, the study demonstrates how dynamic system approaches strengthen trajectory robustness and ease of deployment, offering valuable insights for autonomous decision-making and skill transfer in complex real-world environments.

Long Cheng received the B.S. (Hons.) degree in control engineering from Nankai University, Tianjin, China, in 2004, and the Ph.D. (Hons.) degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009. He is currently a Full Professor with the Institute of Automation, Chinese Academy of Sciences. He is also an adjunct Professor with University of Chinese Academy of Sciences. He has published over 200 technical papers in peer-refereed journals and prestigious conference proceedings. He was a recipient of the IEEE Transactions on Neural Networks Outstanding Paper Award from IEEE Computational Intelligence Society, the Aharon Katzir Young Investigator Award from International Neural Networks Society and the Young Researcher Award from Asian Pacific Neural Networks Society. He is currently serving the Associate Editor/Editorial Board Member of IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Cognitive and Developmental Systems, Science China Information Sciences, Science China Technological Sciences, and Acta Automatica Sinica.  Dr. Cheng is a Fellow of the IEEE/IET.

Speaker: Youqing Wang, Beijing University of Chemical Technology

Title: Theoretical Methods and Industrial Applications of Clustering Analysis

Abstract: Clustering analysis is a representative unsupervised method in machine learning, which attracts widespread attention in many practical fields due to its independence from artificial label information. However, unsupervised setting and increasingly complex data (such as graph data, large-scale multi-view data, etc.) bring significant challenge to the effectiveness of clustering. This presentation would focus on introducing self-supervised information enhanced graph contrastive clustering and large-scale fast multi-view subspace clustering methods, exploring how to mine and utilize data correlations to enhance the clustering process. We further explore the remaining issues and potential industrial applications of clustering analysis.

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 NSFC Distinguished Young Scientists Fund, IET Fellow, the Journal of Process Control Survey Paper Prize, and ADCHEM2015 Young Author Prize. Dr. Wang is (was) the editorial member for nine SCI journals and he is also the member of three technical committees of International Federation of Automatic Control (IFAC).

Speaker: Zhixin Liu, Academy of Mathematics and Systems Science, Chinese Academy of Sciences

Title: Synchronization and Intervention of Multi-Agent Systems with Local Interactions

Abstract: Multi-agent systems widely exist in biological, physical, chemical, social, and engineering systems. How macroscopic collective behaviors emerge from locally interacting multi-agent systems is one of fundamental issues in the field of systems and control.

In this talk, we first talk about the synchronization problem of a basic class of multi-agent systems. In the investigation of multi-agent systems, the model proposed by Vicsek et al. in 1995 (referred to as the Vicsek model) is a basic model due to its computational simplicity and ability to capture key features of complex systems. The synchronization behavior of the Vicsek model has attracted much attention of researchers. However, the complex nonlinear coupling relationship arising from local interactions among agents makes a complete theoretical analysis challenging. Most theoretical results are obtained by relying on connectivity of dynamic graphs. In this talk, we will introduce the random framework and present the synchronization results of the Vicsek model without using a prior connectivity conditions of neighbor graphs by leveraging mathematical tools such as estimation of spectral gap of random geometric graphs and multi-array martingale difference estimation, and further give the smallest possible interaction radius for synchronization which is almost the same as the radius for connectivity of the initial random geometric graphs.

Theoretical analysis of collective behavior reveals that macroscopic behavior emerged from the MAS may not be the desired one. In the second part of the talk, we will focus on the intervention of MAS. We introduce the informed agents into the system, and establish the upper and lower bounds for the proportion of informed agents required for the desired behavior. We further provide theoretical quantitative results for intervention of MAS by two distinct categories of informed agents. These results not only theoretically explain phenomena observed in biological experiments and computational simulations, but also offer insights into the design of algorithms for distributed control, distributed estimation, and distributed optimization of engineering systems.

Zhixin Liu received her Bachelor's degree from the School of Mathematics at Shandong University in 2002, and her Ph.D. from the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences (CAS) in 2007. She is currently a Professor at AMSS, CAS, and serves as the Director of the Key Laboratory of Systems and Control at AMSS, CAS. Her research interests mainly include complex systems, multi-agent systems, distributed estimation, and distributed control.

Dr Liu serves as the Vice President of the Asian Control Association, Associate Vice President of IEEE SMC, Deputy Director of the Technical Committee on Control Theory of the Chinese Association of Automation (CAA), Deputy Director of the Technical Committee on Systems Theory of the Systems Engineering Society of China, and Deputy Director of the Technical Committee on Complex Systems and Complex Networks of the China Society for Industrial and Applied Mathematics (CSIAM). She is also the Deputy Editor-in-Chief of Control Systems and Technology, and the Deputy Editor-in-Chief of Journal of Systems Science and Mathematical Sciences, and serves as the Associate Editor of IEEE Control Systems Letters, and the Associate Editor of Science China Information Sciences.

Speaker: Jianqiang Li, Shenzhen University

Title: Exploring Perception and Decision-Making in Intelligent Systems for the Era of LLMs

Abstract: Multi-agent collaboration and cloud-edge coordination can significantly enhance the perception and execution efficiency of robotic systems. This presentation introduces research on network-coordinated perception, collaborative network construction, and optimized decision-making for robots and intelligent systems. The related findings have been applied to specialized monitoring robots and smart healthcare monitoring systems. Finally, we explore robotic perception and decision-making empowered by LLMs.

Li Jianqiang is a Chair Professor and Tier-2 Professor at Shenzhen University. He is a recipient of the National Science Fund for Distinguished Young Scholars, a Fellow of the Institution of Engineering and Technology (IET Fellow), a doctoral supervisor, the Dean of the School of Artificial Intelligence, and the Executive Director of the National Engineering Laboratory for Big Data System Computing Technology. He also serves as the Chief Scientist of a National Key R&D Program of China and has been selected for the National Young Talents Program. Additionally, he is the Chairman of the Shenzhen Computer Society. His research focuses on robotics and artificial intelligence. He has led one National Key R&D Program of China, one National Natural Science Foundation of China (NSFC) Key Project, and two NSFC General Projects. He has published over 200 papers, including more than 100 in the past five years, with 95 SCI-indexed papers, 56 of which are in Chinese Academy of Sciences (CAS) Tier-1 journals, one in a Science sub-journal, and 50 in IEEE Transactions. Among his publications, 13 are highly cited, two are hot papers, and his work has garnered over 10,000 Google Scholar citations and 6,000 SCI citations.

His patented technologies have been licensed to companies such as ZTE. He has received numerous awards, including the First Prize of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award (1st completer), the First Prize of the Chinese Association of Automation Science and Technology Progress Award (1st completer), and the Second Prize of the Guangdong Provincial Science and Technology Progress Award (1st completer). He founded the Tencent Cloud Artificial Intelligence College at Shenzhen University, which was approved as one of the first batch of National Modern Industry Colleges by the Ministry of Education. This achievement earned him the First-Class Teaching Achievement Award from the Chinese Association for Artificial Intelligence and the Second Prize of the National Teaching Achievement Award. He has also won the Guangdong Provincial Teaching Achievement Award (First Prize) four times.

He serves as an editorial board member for seven journals, including IEEE Transactions, and has been consecutively listed in Stanford University's ranking of the World's Top 2% Scientists for five years.