Cyber-Physical Systems (CPSs) encompass a wide class of complex engineered systems that tightly integrate physical processes with information and communication technologies for sensing, control, optimisation, planning, and management. As CPSs continue to grow in scale, complexity, and autonomy, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful enabling technologies to complement traditional model-based approaches and enhance system performance.

This talk explores how AI and ML can be leveraged to improve the efficiency and effectiveness of modelling, control, and optimisation in CPSs. We begin by examining the fundamental principles of AI and ML from a systems and control perspective, highlighting both their potential and their limitations when applied to safety-critical and real-time environments. We then discuss emerging paradigms that move beyond current CPS and AI/ML frameworks, aiming to better address the inherent spatial and temporal complexities of large-scale, networked systems.

The discussion is grounded in real-world case studies, including the use of AI and ML for detecting money-laundering networks, and the application of generative AI and large language models to enhance control-room operations in smart grids. Through these examples, the talk illustrates how learning-enabled approaches can be effectively integrated with control and systems engineering methodologies to support the development of intelligent, reliable, and scalable CPSs.

Biography: Xinghuo Yu is a Distinguished Professor and Associate Deputy Vice-Chancellor at RMIT University, Melbourne, Australia. He is a Fellow of the Australian Academy of Science, an Honorary Fellow of Engineers Australia, and a Fellow of the IEEE, the International Federation of Automatic Control, the Australian Computer Society, and the Australian Institute of Company Directors. He served as President of the IEEE Industrial Electronics Society in 2018–2019.

Professor Yu received his BEng and MEng degrees from the University of Science and Technology of China, Hefei, in 1982 and 1984, respectively, and his PhD from Southeast University, Nanjing, in 1988. His research interests span control systems, intelligent and complex systems, artificial intelligence and machine learning, and power and energy systems.

Professor Yu’s work has attracted over 63,000 citations on Google Scholar, with an H-index of 118. He has been recognised as a Clarivate Highly Cited Researcher in Engineering every year since 2015. His honours include the 2018 MA Sargent Medal from Engineers Australia, the 2018 Australasian AI Distinguished Research Contribution Award from the Australian Computer Society, and the 2013 Dr.-Ing. Eugene Mittelmann Achievement Award from the IEEE Industrial Electronics Society.