Associate Professor, Faculty of Engineering - Electrical & Computer Engineering Dept
- (780) 492-9100
11-371 Donadeo Innovation Centre For Engineering
9211-116 StEdmonton ABT6G 2H5
Area of Study / Keywords
Control Systems Reinforcement Learning Power Electronics Building Systems Theoretical Biology
Zhan Shu received his B.Eng. degree in Automation from Huazhong University of Science and Technology in 2003, and the Ph.D. degree in Control Engineering from The University of Hong Kong in 2008 under the supervision of Prof. James Lam. For his doctoral research, he received the award for Outstanding Research Postgraduate Student of the University of Hong Kong.
He was a Post-Doctoral Fellow in the Hamilton Institute, National University of Ireland, Maynooth, from 2009 to 2011, working with Prof. Richard H. Middleton, and a Lecturer in the Faculty of Engineering and Physical Sciences, the University of Southampton from 2011 to 2019. Since 2020, he has been a faculty member at the Department of Electrical and Computer Engineering, University of Alberta, where he is currently an Associate Professor.
He is a Senior Member of IEEE, Member of IET, and an invited reviewer of Mathematical Review of the American Mathematical Society. In addition, he serves as an Associate Editor for Mathematical Problems in Engineering, Asian Journal of Control, Journal of The Franklin Institute, Proc. IMechE, Part I: Journal of Systems and Control Engineering, IET Electronics Letters, IET Control Theory and Applications, IEEE Transactions on Automatic Control, and a member of the IEEE Control Systems Society Conference Editorial Board.
My primary research area is control systems, involving both theory and application. In particular, I am interested in connected/networked systems and related cyber-physical fusion. At the heart of my research are developing theories, tools, and methods to address the challenges arising from the connection of physical entities and unlocking the power of cyber-physical fusion. My current research interests include, but are not limited to,
- Networked control
- Learning-based control
- Distributed systems and control
- Hybrid systems
- Autonomous systems
- Stochastic dynamic systems
- Control of power electronic converters (multilevel converters particularly)
- Autonomous operation and control of mobile robots (UAVs and UGVs)
- Energy efficient operation and control of building systems
- A control-theoretic perspective on biological systems (immune system and endocrine system)