Zhengtao Ding
Professor
Control Systems
University of Manchester
United Kingdom
Biography
Prof. Zhengtao Ding graduated with BEng from Tsinghua University, Beijing, China. He then studied control engineering in the Control Systems Centre, UMIST, with MSc in systems and control and PhD in control systems. He joined as a lecturer in School of Engineering, University of Manchester in September 2003 after having been a lecturer in Ngee Ann Polytechnic, Singapore for ten years. He joined the Control Systems Centre, School of Electrical and Electronic Engineering in 2004, after the merger of the UMIST and Victoria University of Manchester, and he is now Professor of Control Systems in The University of Manchester. His research interests are mainly focused on nonlinear and adaptive control theory, and on distributed optimization and control of network-connected dynamic systems in more recent years. His research projects cover various industrial applications, such as distributed optimization in micro grids, formation control of mobile robots and UAVs etc., and his accumulative research funding as the PI is over £3 million. He leads the Sino-UK Joint Advanced Control Technology Laboratory in Manchester. He has published over 200 research papers, and authored a book entitled “Nonlinear and Adaptive Control Systems†published by IET in 2013. He serves as an associate editor for IEEE Transactions on Automatic Control, IEEE Control Systems Letters, Transactions of Institute of Measurement and Control, Journal of Control Theory and Applications, International Journal of Computing and Automation, Unmanned Systems etc.
Research Interest
Nonlinear control systems Adaptive control systems Cooperative and consensus control Disturbance rejection and output regulation with applications Dynamic systems with delay Distributed optimization Fault detection and diagnostics for dynamic systems Control applications to power systems and process control Vibration control andsuppression
Publications
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Continuous Finite-Time Output Regulation of Nonlinear Systems With Unmatched Time-Varying Disturbances
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Off-policy Q-learning: set-point design for optimizing dual-rate rougher flotation operational processes
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Fuzzy disturbance observer based dynamic surface control for air-breathing hypersonic vehicle with variable geometry inlets