Lecture Topic: Sliding Mode: From Control to Optimization and Machine Learning

Speaker: Yu Xinghuo

Date: June 3, 2026

Time: 10:00

Venue: Lecture Hall 333, Electrical Engineering Building

Organizer: School of Electrical and Information Engineering

Speaker Bio

Yu Xinghuo is Pro Vice-Chancellor and Distinguished Professor at RMIT University (Australia), Fellow of the Australian Academy of Science, Honorary Fellow of the Australian Academy of Engineering, and Fellow of IEEE and IFAC. He served as President of the IEEE Industrial Electronics Society from 2018 to 2019. He received his B.Eng. and M.Eng. from the University of Science and Technology of China (Hefei) in 1982 and 1984, respectively, and his Ph.D. from Southeast University (Nanjing) in 1988.

His distinguished contributions have earned numerous awards and honors, including the MA Sargent Medal from Engineers Australia (2018), the Australian AI Distinguished Research Contribution Award from the Australian Computer Society (2018), and the Dr.-Ing. Eugene Mittelmann Achievement Award from the IEEE Industrial Electronics Society (2013). His research expertise spans control systems, intelligent and complex systems, artificial intelligence and machine learning, and power and energy systems.

Lecture Abstract

Sliding mode control has been widely studied and applied due to its robustness and simplicity. Its core lies in the concept of "sliding mode" — introducing a discontinuous control law that forces the system state to converge to a pre-designed manifold with desired dynamic characteristics. Although finite-time convergence to the sliding surface is often regarded as a necessary requirement, traditional sliding-mode design typically achieves only asymptotic stability.

In recent years, new methods introducing finite-time dynamic characteristics, such as terminal sliding mode control, have opened a new chapter in the field. These methods enable finite-time convergence in both the reaching phase and the sliding phase, bringing significant advantages including rapid response, enhanced robustness, and high steady-state precision. This lecture will introduce the fundamental theory of sliding mode control (including terminal sliding mode control), review its development history, and explore the challenges and opportunities it faces. Specifically, it will demonstrate its applications across diverse domains from control systems to global optimization and machine learning.

All faculty and students are welcome!