How to Leverage Machine Learning Tools in Model Predictive Control Schemes

#IEEE #Talk #Toronto #Events #Model #Predictive #Control #Machine #Learning #Artificial #Intelligence

Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on Application of Machine Learning in Model Predictive Control, presented by Dr. Meaghan Charest-Finn.


Tuesday, August 16, 2022 @ 4:00 – 5:00 PM


Abstract: Model Predictive Control (MPC) algorithms provide a convenient entry point for machine learning methods as they are built around a system model. Furthermore, these types 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 can be used to leverage Artificial Intelligence (AI).

  Date and Time




  • Date: 16 Aug 2022
  • Time: 04:00 PM to 05:00 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
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  • Starts 13 June 2022 11:31 PM
  • Ends 15 August 2022 11:31 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
  • No Admission Charge


Meaghan Charest-Finn, PhD Meaghan Charest-Finn, PhD


Dr. Meaghan Charest is an Assistant Professor at the Department of Automotive and Mechatronics Engineering in Ontario Tech University. She teaches topics in mechatronics engineering and her research focuses on developing automation methodologies that practically and responsibly leverage machine learning tools. Much of these methodologies that she has developed build of the Fundamentals of Model Predictive Control and Deterministic System Modeling.