Seminar on "Model Predictive Control and Its Applications in Autonomous Robotic Systems"

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On behalf of the IEEE Industrial Electronics Society Chapter and Student Branch Chapter of the Canadian Atlantic Section, we are pleased to invite you to an upcoming hybrid seminar entitled: “Model Predictive Control and Its Applications in Autonomous Robotic Systems.” The seminar will be presented by Dr. Chao Shen from Carleton University, Canada.
 
Dr. Shen will introduce the fundamental principles and key design considerations of Model Predictive Control (MPC), followed by a discussion of emerging research directions, including multi-objective MPC, data-driven MPC, distributed MPC, and AI-enhanced MPC. Applications and design examples involving autonomous robotic systems will also be presented.
 
Date: Tuesday, July 21, 2026
Time: 10:00 AM–12:00 PM
In-person location: Room C1-359, Sexton Campus, Dalhousie University
 
Zoom Meeting ID: 856 2638 6033
Zoom Password: 3UEQ0w
Zoom link:
https://us05web.zoom.us/j/85626386033?pwd=XTuTmPotgSOOZYhsrk6SWEh8Kfq4pt.1
 
Everyone is welcome to attend, either in person or online. Please feel free to share this invitation with colleagues and students who may be interested.

 


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  • Co-sponsored by IEEE Industrial Electronics Society Student Branch Chapter of the Canadian Atlantic Section
  • Starts 16 July 2026 03:00 AM UTC
  • Ends 21 July 2026 03:00 AM UTC
  • No Admission Charge

  Speakers

Carleton University

Topic:

Model Predictive Control and Its Applications in Autonomous Robotic Systems

Model Predictive Control (MPC) is a widely adopted advanced control strategy with applications across numerous industrial sectors. One of its distinguishing features is its ability to explicitly incorporate system constraints into the control design, providing significant flexibility in formulating and solving control problems. In this talk, following a brief review of relevant optimization and control theories, the fundamental principles of MPC and key design considerations will be discussed in detail. The presentation then highlights several emerging research directions and future trends in MPC, including multi-objective MPC, data-driven MPC, distributed MPC, and AI-enhanced MPC, illustrated through design examples for autonomous robotic systems.

Biography:

Prof. Chao Shen (Member, IEEE) received the B.E. degree in automation engineering and the M.Sc. degree in control science and engineering from Northwestern Polytechnical University, Xi’an, China, in 2009 and 2012, respectively, and the Ph.D. degree in mechanical engineering from the University of Victoria, Victoria, BC, Canada, in 2018.

He is currently an Assistant Professor with the Department of Systems and Computer Engineering, Carleton University Ottawa, ON, Canada. Before joining Carleton University, he was a Postdoctoral Researcher with the Real-time Adaptive Control Engineering Lab, University of Michigan, Ann Arbor, MI, USA. His research interests include control theory, machine learning, and optimization, and their applications in robotics systems, mechatronics systems, and industrial processes. Prof. Shen was the recipient of the 2018 IEEE SMCS Thesis Grant Initiative for his Ph.D. thesis on model predictive control for underwater robotics, and the Natural Science and Engineering Research Council of Canada Postdoctoral Fellowship in 2020. He currently serves as an Associate Editor for IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Informatics, International Journal of Robotics and Automation, and IEEE Canadian Journal of Electrical and Computer Engineering.