Keynote Speaker

Keynote Speaker


Prof. Innocent Kamwa

Laval University, Canada

A full professor in Electrical and Computer Engineering and Tier 1 Canada Research Chair in Decentralized Sustainable Electricity Grids for Smart Communities at Laval University since September 2020, Dr. Kamwa was previously a researcher at Hydro-Québec's Research Institute (IREQ), where for three decades, he specialized in the dynamic performance and control of power systems from a utility perspective. Prior to his retirement from Hydro-Quebec, he was successively, Chief scientist for the Smart grid Innovation Program, Acting Scientific Director and Head of Power system and Mathematics, responsible for the Hydro-Quebec Network Simulation Centre. He was an Adjunct Associate professor at McGill University and Laval University (1991-2020) and the Product Owner and Developer of three widely used power systems simulation tools, namely EMTP, SimpowerSystems and Hypersim, commercialized by EMP-Alliance, Mathworks and OPAL-RT, respectively. 

Dr. Kamwa received a PhD in Electrical Engineering in 1989 from Laval University, and became a 2005 IEEE Fellow for "contributions to synchronous machines identification and innovations in power grid control". He is both the 2019 IEEE Charles Proteus Steinmetz Technical Field Award , “For sustained leadership in the development of standards for electrical machines”, and the 2019 Charles Concordia Power System Engineering Award, “For contributions in enhancing power grid performance by novel measurement devices, identification techniques, and stability control systems”. He is also a recipient of four IEEE PES best paper prize awards (1998, 2003, 2009, 2012, 2022), and four IEEE PES outstanding working group awards (1998, 2006, 2011, 2013). He is a member of Canada Academy of Engineering and International Member of US National Academy of Engineering “For contributions to adaptive power grid control schemes and synchronous generator testing and standards.”


Prof. Simon X. Yang

University of Guelph, Canada

Prof. Simon X. Yang received the B.Sc. degree in engineering physics from Beijing University, China in 1987, the first of two M.Sc.  degrees in biophysics from Chinese Academy of Sciences, Beijing, China in 1990, the second M.Sc. degree in electrical engineering from the University of Houston, USA in 1996, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, Canada in 1999. Prof. Yang joined the School of Engineering at the University of Guelph, Canada in 1999. Currently he is a Professor and the Head of the Advanced Robotics & Intelligent Systems (ARIS) Laboratory at the University of Guelph in Canada. 

Prof. Yang has diversified research expertise. His research interests include robotics, intelligent systems, control systems, sensors and multi-sensor fusion, wireless sensor networks, intelligent communication, intelligent transportation, machine learning, and computational neuroscience. Prof. Yang he has been very active in professional activities. Prof. Yang serves as the Editor-in-Chief of International Journal of Robotics and Automation, and an Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Artificial Intelligence, and several other journals. He has involved in the organization of many international conferences.


Prof. Prashant Mhaskar

McMaster University , Canada

Prashant Mhaskar is a professor in the department of Chemical Engineering at McMaster University. His research interests include nonlinear model predictive control and fault tolerant control and data driven batch process modeling and control. Research results from Dr. Mhaskar have resulted in over 140 journal articles, and two monographs. He enjoys teaching, mentoring and learning from his HQP and has supervised 2 PDFs, 16 Phd, 11 MASc and over 50 undergraduates students so far and currently works with 1 PDF, 9 Phd,  4 Masters and 4 undergraduate students. His research on data driven (and hybrid model based) control has been fueled by and developed in collaboration with industrial partners such as Johnson Controls Inc., Praxair Inc., Hydromantis, Sartorius Inc., Corning Inc., Imperial Oil and Honeywell. Over his career he held the Canada Research Chair in Nonlinear and Fault-Tolerant Control and the McMaster University Scholar award. He was awarded the D.G. Fisher award in 2022. He has served on and chaired the discovery grant committee in the past and presently serves as an Associate Editor for Automatica. 

Title: A Practical Reinforecment Learning Based Model Predictive Control Design.

Abstract: Reinforcement learning (RL) shows promising potential for process control applications, and is of interest to the industrial sector. In many practical situations, a limited amount of information (step-test data) is available to develop an optimal control strategy, such as dynamic matrix control (DMC), one of the most popular methods of model predictive control (MPC). Utilization of MPC techniques to warm start RL implementations have remained limited. The talk will present recent advances in the area of practically implementable Reinforcement Learning Agent based control designs.