Refreshments will also be arranged during the presentation session; courtesy - Dean's office (Faculty of Eng.).
Title: Iterative Learning Control and its applications in Rehabilitation Robotic Systems
Iterative learning control (ILC) comes from the idea of “practices makes perfect”. By exploiting the experiences learned from the previous task, ILC algorithms can gradually improve the performance without knowing much about the engineering systems. Due to its data-driven nature, ILC has been successfully applied to many industrial processes. This talk will start from a brief introduction of ILC followed by sharing our experience of ILC algorithms in rehabilitation robotic systems for the recovery post-stroke patients. In particular, the concept of “assist-as-needed” to speed up the recovery procedure of individual patient by tuning the level of assistance will be discussed. Some theoretical analysis will be presented with interesting simulation results.
Ying Tan is an Associate Professor and Reader in the Department of Mechanical Engineering at the University of Melbourne, Australia.
She received her Bachelor’s degree from Tianjin University, China, in 1995, and her PhD from the Department of Electrical and Computer Engineering, National University of Singapore in 2002. She joined McMaster University in 2002 as a postdoctoral fellow in the Department of Chemical Engineering. Since 2004, she has been with the University of Melbourne. She was awarded an Australian Postdoctoral Fellow (2006-2008) and a Future Fellow (2009-2013) by the Australian Research Council. Her research interests are in intelligent systems, nonlinear control systems, real time optimisation, sampled-data distributed parameter systems and formation control.