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DTSTART:20260329T030000
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DTSTART:20261025T020000
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DTSTAMP:20260508T224919Z
UID:8BC8731A-3ABD-45CE-9FF4-157B3DA9F6C1
DTSTART;TZID=Europe/Copenhagen:20260615T140000
DTEND;TZID=Europe/Copenhagen:20260615T150000
DESCRIPTION:Title:\nAI-Based Real-Time Control of Power Electronic Circuits
  on Microcontrollers\n\nAbstract:\nArtificial intelligence is increasingly
  entering the field of power electronics\, but its use for real-time contr
 ol remains challenging due to strict requirements on timing\, robustness\,
  and embedded implementation.\n\nThis webinar will introduce the motivatio
 n for AI-based controllers with a focus on reinforcement learning and comp
 are their potential and limitations with established control approaches.\n
 \nA practical workflow for developing and deploying reinforcement-learning
 -based controllers on resource-constrained microcontrollers will be presen
 ted\, covering simulation-based training\, embedded implementation\, real-
 time constraints\, and experimental validation.\n\nMeasurement results fro
 m power electronic circuits will be used to discuss the transition from si
 mulation to hardware.\n\nThe webinar will conclude with open challenges an
 d future directions\, including robustness\, safety\, hybrid AI-classical 
 control\, and the role of AI at the edge for next-generation power electro
 nic systems.\n\nSpeaker:\nProf. Christian Schumann\nKaiserslautern Univers
 ity of Applied Sciences\, Germany\n\nVirtual: https://events.vtools.ieee.o
 rg/m/558843
LOCATION:Virtual: https://events.vtools.ieee.org/m/558843
ORGANIZER:sssa@energy.aau.dk
SEQUENCE:80
SUMMARY:AI-Based Real-Time Control of Power Electronic Circuits on Microcon
 trollers
URL;VALUE=URI:https://events.vtools.ieee.org/m/558843
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Title:&lt;br&gt;AI-Based Real-Time Control of Po
 wer Electronic Circuits on Microcontrollers&lt;/p&gt;\n&lt;p&gt;Abstract:&lt;br&gt;Artificia
 l intelligence is increasingly entering the field of power electronics\, b
 ut its use for real-time control remains challenging due to strict require
 ments on timing\, robustness\, and embedded implementation.&lt;/p&gt;\n&lt;p&gt;This w
 ebinar will introduce the motivation for AI-based controllers with a focus
  on reinforcement learning and compare their potential and limitations wit
 h established control approaches.&lt;/p&gt;\n&lt;p&gt;A practical workflow for develop
 ing and deploying reinforcement-learning-based controllers on resource-con
 strained microcontrollers will be presented\, covering simulation-based tr
 aining\, embedded implementation\, real-time constraints\, and experimenta
 l validation.&lt;/p&gt;\n&lt;p&gt;Measurement results from power electronic circuits w
 ill be used to discuss the transition from simulation to hardware.&lt;/p&gt;\n&lt;p
 &gt;The webinar will conclude with open challenges and future directions\, in
 cluding robustness\, safety\, hybrid AI-classical control\, and the role o
 f AI at the edge for next-generation power electronic systems.&lt;/p&gt;\n&lt;p&gt;Spe
 aker:&lt;br&gt;Prof. Christian Schumann&lt;br&gt;Kaiserslautern University of Applied 
 Sciences\, Germany&lt;/p&gt;
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