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Keynote Speeches

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Professor Ying Tan, IEEE Fellow

The University of Melbourne

Learning  Control and Its Application in Rehabilitation Robotics

Abstract: 

Rehabilitation robotics leverages the principle of "practice makes perfect" by using repetitive task-based exercises to facilitate motor re-learning and functional recovery, particularly in post-stroke rehabilitation. Rooted in neurocognitive rehabilitation theories, robot-assisted therapies provide tailored, intensive training routines that meet individual patient needs. Learning control (LC) strategies, originally developed in 1978 to achieve high tracking performance in industrial applications, offer a compelling framework for controller designs in this field. Unlike traditional control methods, LC algorithms improve performance over time by utilizing information from previous iterations. This talk highlights recent advances in LC designs and illustrates how various LC algorithms effectively address the unique challenges posed by rehabilitation robotics. Additionally, it explores future opportunities for integrating learning control into rehabilitation systems and outlines key research questions for advancing control theory in this critical area.

Biography:
Dr. Ying Tan is a Professor in Mechanical Engineering at The University of Melbourne, Australia. She earned her bachelor's degree from Tianjin University, China, in 1995, and her PhD from the National University of Singapore in 2002. After a postdoctoral fellowship at McMaster University, she joined The University of Melbourne in 2004. Dr. Tan has received prestigious recognitions, including an Australian Postdoctoral Fellowship (2006-2008) and an ARC Future Fellowship (2009-2013). Currently, she serves on the ARC College of Experts (2024-2026) and holds several distinguished titles, including Fellow of IEEE (FIEEE), Engineers Australia (FIEAust), and the Asia-Pacific Artificial Intelligence Association. She is also a member of the IEEE Fellow Committee (2024-2025). Her research spans intelligent systems, nonlinear systems, data-driven optimization, rehabilitation robotics, human motor learning, wearable sensors, and model-guided machine learning.

 

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Professor Zhihong Man

Swinburne University of Technology

Further study of lateral dynamics of road vehicles

Abstract: 

In this talk, the lateral dynamics of road vehicles (LDRVs) is further studied from the view point of vehicle informatics. It is seen that the LDRVs is first decoupled in s-domain and the vehicle slip angle is proved to be directly observed or extracted from the yaw rate measurements. A new framework of parameter estimation methodology with the steady state information of yaw rate is then proposed, to accurately estimate the parameters of LDRVs in any fundamental period of sinusoidal steering angle input. Our new findings in this work will play a very important role of developing next generation of state estimation, parameter identification, soft-sensing, stability control and fault diagnosis of intelligent road vehicles. The simulation results are demonstrated to show the advantages and effectiveness of the new research results for LDRVs.

Biography:

 
   Zhihong received his B.E. degree from Shanghai Jiaotong University, China, in 1982,  M.Sc degree from Chinese Academy of Sciences in 1987, and PhD degree from the University of Melbourne, Australia, in 1994. From 1994 to 1996, Zhihong was the Lecturer in the Department of Computer and Communication Engineering at Edith Cowan University, Australia. From 1996 to 2001, he was the Lecturer and then Senior Lecturer in the School of Engineering at The University of Tasmania, Australia. From 2002 to 2007, he was the Associate Professor of Computer Engineering at Nanyang Technological University, Singapore. From 2007 to 2008, he was with Monash University Sunway Campus, served as the Professor and Head of Electrical and Computer Systems Engineering and the Chair of the Monash Sunway Campus Research Committee. Zhihong is currently the Professor of Engineering in the School of Science, Computing and Engineering Technologies at Swinburne University of Technology, Melbourne, Australia.
   Zhihong’s research interests are in nonlinear control, signal processing, robotics, neural networks, engineering optimization and vehicle dynamics & control. He has published more than 300 research papers in refereed international journals and refereed international conferences proceedings, and his research results have widely cited more than 18600 times by the researchers from many countries. Since 1994, Zhihong has been involved in many international conferences in control, robotics, signal processing, neural networks and industrial electronics as the General Chair, Program Committee Chair, Track Chair, Session Chair, and the International Advisory Committee and Technical Committees member.

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Dr John Vial

Nexxis, Perth, Australia

Robotics Research & Industrial Innovation: How To Find And Work With Industry Partners

Abstract: 

From the perspective of researchers, industry partners just look like big bags of money, its easy to think: "Surely they will want to support important my research? Imagine the new products they will make from it?" In this talk I'll describe my experience from the other side of the table. How industrial partners think about innovation, the kinds of drivers that they face, and what you should do if you want to build a long lasting, trusted business partnership. Along the way we will also look at current robotics trends, such as humanoid robots and what they could mean for industry partnerships going forward. 

Biography:

 
  John Vial is a seasoned expert in robotics and machine learning, dedicated to the practical application of these transformative technologies. As the Project Execution Lead at Nexxis, John is at the forefront of developing advanced legged, magnetic inspection robots. His technical leadership spans across roles, including major AI initiatives at Fortescue Metals Group, where he was integral to the conception of an autonomous light vehicle project among other innovative solutions. John holds a PhD in Robotics from ACFR at Sydney University.

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