报告人简介：

Ying Tan obtained her PhD degree in Electrical Engineering from National University of Singapore in 2002. After that, she joined the Department of Chemical Engineering, McMaster University as a postdoctoral fellow. Since 2004, she has been with Department of Electrical and Electronic Engineering, the University of Melbourne. She is currently an Associate Professor and Reader.  Ying Tan was awarded an Australian Postdoctoral Fellow (2006-2008) and a Future Fellow (2009-2013) by the Australian Research Council.  She has published a large number of journal and conference papers and books. She currently is an Associate Editor for the journals “IEEE Transactions on Automatic Control”, “Systems and Control Letters” and “Asian Journal of Control”.  Her current research interests are Iterative Learning Control, Extremum Seeking Control, Nonlinear Systems and applications in rehabilitation robotic systems and engine operation optimizations.

报告摘要:

The key idea of iterative learning control ( ILC) is captured by the saying “ Practice makes perfect”.    Whenever the task is of repetitive nature, the opportunity exists to improve the task execution in the next iteration based on the observations of the past attempt. ILC has been widely used in many industries processes such as wafer manufacturing processes, robotics, batch processes and so on. This talk will summarize our recent progress in applying the concept of ILC in post-stroke rehabilitation robotics systems. In particular, the concept of assistance as needed will be introduced to design the robotic assistance to speed up the procedure of the recovery. Our analysis shows that neither too much assistance nor too little assistance will encourage humans or patients to learn. An optimal assistance thus exists. Either off-line optimization techniques or on-line optimization techniques can be used to find this optimal assistance. This technique will provide a design guideline in our newly designed rehabilitation robotic system: Emu.