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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20260513T152430Z
UID:3E48B962-46C2-4F40-ADE8-E1A7C68303CF
DTSTART;TZID=America/Los_Angeles:20251210T170000
DTEND;TZID=America/Los_Angeles:20251210T183000
DESCRIPTION:Abstract:\n\nRehabilitation robotics leverages the principle of
  &quot;practice makes perfect&quot; by using repetitive task-based exercises to faci
 litate motor re-learning and functional recovery\, particularly in poststr
 oke rehabilitation. Rooted in neurocognitive rehabilitation theories\, rob
 ot-assisted therapies provide tailored\, intensive training routines that 
 meet individual patient needs. Learning control (LC) strategies\, original
 ly developed in 1978 to achieve high tracking performance in industrial ap
 plications\, offer a compelling framework for controller designs in this f
 ield. Unlike traditional control methods\, LC algorithms improve performan
 ce over time by utilizing information from previous iterations. This talk 
 highlights recent advances in LC designs and illustrates how various LC al
 gorithms 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 qu
 estions for advancing control theory in this critical area.\n\nSpeaker(s):
  Prof. Tan\, \n\nVirtual: https://events.vtools.ieee.org/m/519794
LOCATION:Virtual: https://events.vtools.ieee.org/m/519794
ORGANIZER:xulai366@hotmail.com
SEQUENCE:15
SUMMARY:Learning Control and Its Application in Rehabilitation Robotics
URL;VALUE=URI:https://events.vtools.ieee.org/m/519794
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;xmsonormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;fo
 nt-size: 11.0pt\;&quot;&gt;Abstract: &lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;xmsonormal&quot;&gt;&lt;
 span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Rehabilitation roboti
 cs leverages the principle of &quot;practice makes perfect&quot; by using repetitive
  task-based exercises to facilitate motor re-learning and functional recov
 ery\, particularly in poststroke rehabilitation. Rooted in neurocognitive 
 rehabilitation theories\, robot-assisted therapies provide tailored\, inte
 nsive training routines that meet individual patient needs. Learning contr
 ol (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 pr
 evious iterations. This talk highlights recent advances in LC designs and 
 illustrates how various LC algorithms effectively address the unique chall
 enges 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&lt;/span&gt;&lt;span style=&quot;font-family: &#39;Arial Narrow&#39;\,sans-serif\;
 &quot;&gt;.&lt;/span&gt;&lt;/p&gt;
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