Prof. LI Hanxiong (李涵雄)

Education:

• Ph.DUniversity of Auckland, New Zealand, 1997
• M.E.Delft University of Technology, The Netherlands, 1991
• B.E.National University of Defence Technology, Hunan, China, 1982

Working Experience:

• Professor (2008), Associate Professor (2002), Assistant Professor (2000), RAP(1997), Department of MEEM, City University of Hong Kong
• Senior Process Engineer, ASM Assembly Automation Ltd, Hong Kong, 1997
• Research Engineer, University of Auckland, New Zealand (1993-1997)
• Research Associate, Delft University of Technology, The Netherlands (1991-1992)
• Project Manager, China International Trust & Investment Co (CITIC), Beijing, China, (19987-1988)
• Software Engineer, Peking Analysis Center (military service), China, (1982-1987)

• IEEE Fellow
• Distinguished Expert by China Federation of Returned Overseas (中國僑聯特聘專家)（2011- ）
• Distinguished Expert by Hunan Government, China (湖南省特聘專家)（2010- ）
• Awarded Thousand Talents Scheme in China (千人计划) (2010-2015)
• Awarded Cheung Kong Scholar (长江学者) by Ministry of Education, China (2006-2009)
• Awarded China National Science Fund for Distinguished Young Scholars (overseas杰青B) in 2004
• 2^nd prize of Natural Science Award from Ministry of Education, China (2008)
  (教育部自然科學二等獎)
• 2^nd prize of Science & Tech Development Award from Hunan Government, China (2007)
  (湖南省科技進步二等獎)
• Best Dissertation Award (2011)
  Lu XinJiang (陸新江) “Model-based robust design and its integration with control (基於模型的魯棒設計及其控制的集成研究)” - Hiwin Doctoral Dissertation Award 『首屆上銀優秀機械博士論文優秀獎』

• Associate Editor, IEEE Transactions on Systems, Man & Cybernetics - Systems, (2016 - )
• Associate Editor, IEEE Transactions on Cybernetics, (2002 - 2016)
• Associate Editor, IEEE Transactions on Industrial Electronics (2009- 2015)
• Editorial board, Control Engineering (2004 - )
• Editorial board, Frontiers of Mechanical Engineering in China (2008 - )

• Intelligent control & learning
• Process Design and Control
• Electronic Packaging Process
• Distributed Parameter System 

Three-Domain Fuzzy Logic Control System (3D-FLC)

• A spatial domain is added into the traditional FLC to formulate a 3D fuzzy platform for the spatial-temporal process. With the newly designed 3D fuzzy membership functions and 3D inference engine, this spatial-temporal FLC has the inherent capability to handle the spatial distribution with the help of only a few more sensors.
• A probabilistic domain is added into the traditional FLC to formulate a 3D fuzzy platform for the stochastic process. After embedding the probabilistic processing into the Mamdani fuzzy inference, this probabilistic fuzzy logic system can model both uncertainty and stochastic variations.

Intelligent Modeling and Control of Distributed Parameter Systems (DPS)

• Development of various modeling methods, including novel 3D kernels (Volterra, Hammerstein, and Wiener) based approach, and nonlinear time-space separation approach, for the nonlinear DPS. For engineering application, a spectral/neural modeling method is developed for estimating the temperature field using fewer sensors.
• It is the first time to design Takagi-Sugeno (T-S) fuzzy system for control of DPS and integrate the neural system for learning of the unknown nonlinearities under different unknown working conditions.

Process Design and Control

Development of integrated approaches for robust design for both static and dynamic system under various uncertainties with help of the perturbation theory, and control theory, which includes

• Variable sensitivity robust design for unknown process;
• Multi-objective optimization based robust design under model uncertainties;
• Stability-based robust design for dynamic system under model uncertainty;
• Intelligent integration of design and control for complex process

Design and Control for IC Packaging Process

• Development of a high-precision epoxy dispensing system that is able to compensate the fluid variation in the operation. The work involves the proper integration of the Non-Newtonian fluid modeling and approximation, sensing, run-by-run control.
• Snap cure oven design and control aims to have a required temperature field under the strong uncertainties. A simple but effective model needs to predict the temperature field in the spatial domain with fewer sensors, upon which the robust design and optimal control can be developed.

Fuzzy-PID Design & Tuning

• Designed a two-input fuzzy-PID controller for process control and developed various gain tuning methods the controller.
• Improved the graphical technique for the quantitative model derivation, disclosed and proved the sliding-mode feature of the fuzzy-PID.

Professional Expertise - Knowledge Transfer Office (KTO)