BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Australia/Melbourne
BEGIN:DAYLIGHT
DTSTART:20251005T030000
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10
TZNAME:AEDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20260405T020000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4
TZNAME:AEST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251207T233347Z
UID:6562F527-1CCB-41EE-A556-B52124B3325E
DTSTART;TZID=Australia/Melbourne:20251204T005900
DTEND;TZID=Australia/Melbourne:20251204T010100
DESCRIPTION:Rehabilitation robotics leverages the principle of "practice ma
 kes perfect" by using repetitive task-based exercises to facilitate motor 
 re-learning and functional recovery\, particularly in poststroke rehabilit
 ation. Rooted in neurocognitive rehabilitation theories\, robot-assisted t
 herapies provide tailored\, intensive training routines that meet individu
 al 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 re
 cent advances in LC designs and illustrates how various LC algorithms effe
 ctively address the unique challenges posed by rehabilitation robotics. Ad
 ditionally\, it explores future opportunities for integrating learning con
 trol into rehabilitation systems and outlines key research questions for a
 dvancing control theory in this critical area.\n\nCo-sponsored by: Dept. E
 lectrical & Electronic Engineering\, The University of Melbourne\n\nSpeake
 r(s): Prof Ying Tan from the University of Melbourne\n\nLevel 2\, Building
  193 (EEE)\, The University of Melbourne\, Parkville\, Victoria\, Australi
 a\, 3010\, Virtual: https://events.vtools.ieee.org/m/519148
LOCATION:Level 2\, Building 193 (EEE)\, The University of Melbourne\, Parkv
 ille\, Victoria\, Australia\, 3010\, Virtual: https://events.vtools.ieee.o
 rg/m/519148
ORGANIZER:ye.pu@unimelb.edu.au
SEQUENCE:5
SUMMARY:IEEE CSS Distinguished Lecture: Learning Control and Its Applicatio
 n in Rehabilitation Robotics
URL;VALUE=URI:https://events.vtools.ieee.org/m/519148
X-ALT-DESC:Description: <br /><p>Rehabilitation robotics leverages the prin
 ciple of "practice makes perfect" by using repetitive task-based exercises
  to facilitate motor re-learning and functional recovery\, particularly in
  poststroke rehabilitation. Rooted in neurocognitive rehabilitation theori
 es\, robot-assisted therapies provide tailored\, intensive training routin
 es that meet individual patient needs. Learning control (LC) strategies\, 
 originally developed in 1978 to achieve high tracking performance in indus
 trial applications\, offer a compelling framework for controller designs i
 n this field. Unlike traditional control methods\, LC algorithms improve p
 erformance over time by utilizing information from previous iterations. Th
 is talk highlights recent advances in LC designs and illustrates how vario
 us LC algorithms effectively address the unique challenges posed by rehabi
 litation robotics. Additionally\, it explores future opportunities for int
 egrating learning control into rehabilitation systems and outlines key res
 earch questions for advancing control theory in this critical area.</p>
END:VEVENT
END:VCALENDAR