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DTSTAMP:20260624T231219Z
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DTSTART;TZID=America/Denver:20260626T110000
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DESCRIPTION:Electric motor drives are widely used in industrial systems\, e
 lectric vehicles\, and renewable energy applications\, where high efficien
 cy\, fast dynamic response\, and robustness are important. Conventional co
 ntrol methods\, such as field-oriented control (FOC)\, provide reliable pe
 rformance but depend on accurate system models and careful tuning.\n\nTo a
 ddress these limitations\, Model Predictive Control (MPC) has been propose
 d as an alternative\, offering improved dynamic performance and the abilit
 y to directly handle system constraints. However\, model-based MPC is stil
 l sensitive to parameter mismatch and modelling errors\, which can affect 
 performance in practice.\n\nTo improve robustness\, data-driven and model-
 free approaches have been explored\, where system behaviour is estimated o
 nline from measured data rather than relying fully on predefined models. T
 hese methods aim to reduce dependence on parameter accuracy\, although the
 y introduce new challenges such as sensitivity to measurement noise and re
 al-time implementation.\n\nThis presentation looks at different control st
 rategies for electric drives\, from classical methods to predictive and da
 ta-driven approaches\, and discusses how they relate to practical industri
 al applications.\n\nCo-sponsored by: Resilience and Clean Energy Systems (
 RCES)\n\nSpeaker(s): Joseph O. Akinwumi \n\nVirtual: https://events.vtools
 .ieee.org/m/565078
LOCATION:Virtual: https://events.vtools.ieee.org/m/565078
ORGANIZER:bli4@ualberta.ca
SEQUENCE:7
SUMMARY:Electric Motor Drives in Industry: Control Methods\, challenges and
  the role of predictive control
URL;VALUE=URI:https://events.vtools.ieee.org/m/565078
X-ALT-DESC:Description: <br /><div>Electric motor drives are widely used in
  industrial systems\, electric vehicles\, and renewable energy application
 s\, where high efficiency\, fast dynamic response\, and robustness are imp
 ortant. Conventional control methods\, such as field-oriented control (FOC
 )\, provide reliable performance but depend on accurate system models and 
 careful tuning.</div>\n<p>To address these limitations\, Model Predictive 
 Control (MPC) has been proposed as an alternative\, offering improved dyna
 mic performance and the ability to directly handle system constraints. How
 ever\, model-based MPC is still sensitive to parameter mismatch and modell
 ing errors\, which can affect performance in practice.</p>\n<p>To improve 
 robustness\, data-driven and model-free approaches have been explored\, wh
 ere system behaviour is estimated online from measured data rather than re
 lying fully on predefined models. These methods aim to reduce dependence o
 n parameter accuracy\, although they introduce new challenges such as sens
 itivity to measurement noise and real-time implementation.&nbsp\;</p>\n<p>
 This presentation looks at different control strategies for electric drive
 s\, from classical methods to predictive and data-driven approaches\, and 
 discusses how they relate to practical industrial applications.</p>
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