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DTSTART:20220313T030000
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DTSTART:20221106T010000
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DTSTAMP:20220816T214432Z
UID:446F42F7-07D0-42BC-944D-416563EBB810
DTSTART;TZID=America/New_York:20220816T160000
DTEND;TZID=America/New_York:20220816T170000
DESCRIPTION:Join the IEEE Toronto Instrumentation &amp; Measurement – Robotic
 s &amp; Automation Joint Chapter for a talk on Application of Machine Learning
  in Model Predictive Control\, presented by Dr. Meaghan Charest-Finn.\n\nT
 uesday\, August 16\, 2022 @ 4:00 – 5:00 PM\n\nAbstract: Model Predictive
  Control (MPC) algorithms provide a convenient entry point for machine lea
 rning methods as they are built around a system model. Furthermore\, these
  types of constrained control algorithms are robust\, well suited for Mult
 iple-Input Multiple-Output (MIMO) Systems and Nonlinear Systems. In this t
 alk we will discuss fundamental concepts of MPC and how the model componen
 t can be used to leverage Artificial Intelligence (AI).\n\nSpeaker(s): Mea
 ghan Charest-Finn\, PhD\, \n\nVirtual: https://events.vtools.ieee.org/m/31
 7037
LOCATION:Virtual: https://events.vtools.ieee.org/m/317037
ORGANIZER:s.sedghizadeh.ca@ieee.ca
SEQUENCE:11
SUMMARY:How to Leverage Machine Learning Tools in Model Predictive Control 
 Schemes
URL;VALUE=URI:https://events.vtools.ieee.org/m/317037
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;Default&quot; style=&quot;line-height: 115%\;
 &quot;&gt;Join the IEEE Toronto Instrumentation &amp;amp\; Measurement &amp;ndash\; Roboti
 cs &amp;amp\; Automation Joint Chapter for a talk on Application of Machine Le
 arning in Model Predictive Control\, presented by &lt;strong&gt;Dr. Meaghan Char
 est-Finn&lt;/strong&gt;.&lt;/p&gt;\n&lt;p class=&quot;Default&quot; style=&quot;line-height: 115%\;&quot;&gt;&amp;nb
 sp\;&lt;/p&gt;\n&lt;p class=&quot;Default&quot; style=&quot;line-height: 115%\;&quot;&gt;&lt;span style=&quot;colo
 r: #000000\; font-size: 14pt\;&quot;&gt;&lt;strong&gt;Tuesday\, August 16\, 2022 @ 4:00 
 &amp;ndash\; 5:00 PM&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;Default&quot; style=&quot;line-heigh
 t: 115%\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Model Predictive Con
 trol (MPC) algorithms provide a convenient entry point for machine learnin
 g methods as they are built around a system model. Furthermore\, these typ
 es of constrained control algorithms are robust\, well suited for Multiple
 -Input Multiple-Output (MIMO) Systems and Nonlinear Systems. In this talk 
 we will discuss fundamental concepts of MPC and how the model component ca
 n be used to leverage Artificial Intelligence (AI).&lt;/p&gt;
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