Keynote Speech 01——Xudong Zhao

Time Driven-Based Switching Control of Switched Systems

Professor and Doctoral Supervisor at Dalian University of Technology, the Deputy Director of the " Key Laboratory of Intelligent Control and Optimization for Industrial Equipment," and a selectee of the National Special Support Program for High-Level Talents (Leading Talent)

Abstract:

The plant models of many control systems exhibit the characteristics of multi-"mode" switching. As a significant class of multi-mode nonlinear systems, switched systems provide a unified mathematical modeling framework for such systems. Concurrently, research into the control problems of switched systems can offer novel perspectives for resolving other challenging issues in control theory. Due to their distinct engineering background and broad application prospects, the control of switched systems has emerged as a major research hotspot in recent years. Time-driven switching control is a crucial approach in the control of switched systems. This report will present several theoretical results regarding the design of time-driven switching control for switched systems, primarily including: the application of the mode-dependent average dwell time switching method, the admissible edge-dependent average dwell time switching method, and the multiple discrete Lyapunov function method in the time-driven switching design for both linear and nonlinear switched systems.

Biography:

Xudong Zhao is a Professor and Doctoral Supervisor at Dalian University of Technology, the Deputy Director of the " Key Laboratory of Intelligent Control and Optimization for Industrial Equipment," and a selectee of the National Special Support Program for High-Level Talents (Leading Talent). In recent years, he has achieved a series of research results in the stability, robust control, and intelligent control of switched systems, uncertain systems, and several classes of nonlinear systems, as well as their applications in fields such as aero-engines and robotics. He has published over 160 papers in Automatica and the IEEE Transactions series journals, including more than 20 in the top-tier control journals including Automatica and IEEE TAC. His research results have been cited over 20,000 times, with multiple papers selected as ESI Highly Cited Papers. He has presided over numerous key research projects, including the National Science and Technology Major Project (as Chief Scientist), National Key R&D Program, Key Program of the National Natural Science Foundation of China, National Science Fund for Excellent Young Scholars, a research project supported by the Equipment Development Department of the Central Military Commission, and research topics of the Aero-Engine and Gas Turbine Major Project. Furthermore, he is a recipient of the Highly Cited Researcher Award (Web of Science), the USERN Prize for Young Scientists, and the Young Scientist Award from the Chinese Association of Automation (CAA); he has won eight scientific and technological awards, including the Second Prize of the Natural Science Award by the Ministry of Education and the First Prize of the Natural Science Award by the CAA. He has also published two English monographs and holds over 10 authorized national invention patents. He serves as a Standing Committee Member of both the CICC Technical Committee on Intelligent Control and Systems and the CICC Technical Committee on Swarm Intelligence and Cooperative Control, a Committee Member of the Technical Committee on Engine Control Technology, China Aerospace Propulsion Consortium, and a Committee Member of the Technical Committee on Robotics of the Chinese Mechanical Engineering Society. Additionally, he serves on the editorial boards for Acta Automatica Sinica, Control Engineering, and SCI-indexed journals including IEEE Transactions on Systems, Man, and Cybernetics: Systems and Nonlinear Analysis: Hybrid Systems, while also serving as a member of the Advisory Board for Engineering Reports.

Keynote Speech 02——Jan Lundgren

Measurement for AI, AI for Measurement: Rethinking Measurement Systems in the Age of Artificial Intelligence

Professor at Mid Sweden University, Sundsvall, Sweden, on the department of Computer and Electrical Engineering

Abstract:

Artificial intelligence is increasingly intertwined with sensing and measurement, challenging the traditional view of AI as a downstream tool applied to collected data. Measurement strategies, sensor configurations, data quality and sensor fusion directly influence the performance of subsequent AI models. At the same time, AI can enhance, reconstruct and fuse measurement data, enabling improved measurements and new sensing capabilities. This keynote explores this bidirectional relationship through the perspectives of measurement for AI and AI for measurement, and argues for a transition toward measurement with AI, where sensing, measurement, data fusion and artificial intelligence are considered parts of one interconnected system. Drawing on examples from intelligent measurement applications, the talk discusses the opportunities created by this system-level perspective and concludes by raising a fundamental question for the measurement community: What does it mean for an AI-supported measurement system to be accurate?

Biography:

Jan Lundgren is a Full Professor at Mid Sweden University, Sweden, where he leads a research group focusing on AI-supported sensor systems. He received his Ph.D. in Electronics from Mid Sweden University in 2007 and has since held positions as Assistant Professor and Associate Professor before being promoted to Full Professor in 2024. He has also been a Visiting Professor at the University of Salerno, Italy, and a Guest Researcher at the Norwegian University of Science and Technology (NTNU), Norway.

His research focuses on the interplay between artificial intelligence, sensing and measurement systems, with particular emphasis on AI-supported measurement, sensor and data fusion, acoustic sensing, computer vision, medical measurement systems, and industrial applications. A central theme of his research is how measurement strategies and data quality influence AI performance, and conversely, how AI can enhance measurement quality and enable new sensing capabilities. He has authored and co-authored more than 70 scientific publications in international journals and conference proceedings and has led several research projects in AI-supported sensing and measurement. He is a member of the IEEE Instrumentation and Measurement Society.