Professor of Chemical Engineering, Queen’s University
Dr. Martin Guay is a professor in the Department of Chemical Engineering at Queen’s University. Dr. Guay’s research interests are in the area of process control, control theory and applied statistics. Dr. Guay received the Queen’s University Chancellor Research Award and the Premier Research Excellence Award. He also received the Syncrude Innovation Award and the D.G. Fisher Award from the Canadian Society of Chemical Engineers. In 2011, he received, with Dr. Veronica Adetola, the Best Paper Award (Theory), Journal of Process Control (2008-2011). He is a Fellow of the Chemical Institute of Canada. He is the Editor-in-Chief of Journal of Process Control and Senior Editor for the IEEE Control Systems Society Letters. He is an Associate Editor for Automatica and the IEEE Transactions on Automatic Control. He is also a review editor for the Canadian Chemical Engineering Journal.
Title: Data-driven control of unknown nonlinear systems using extremum seeking.
The complexity of system dynamics can often be an obstacle in the development of reliable dynamical models. In classical control engineering methodologies, the knowledge of the system’s dynamics has always been a key element in the design, testing and implementation of control systems. Since the development of reliable dynamical models is often restrictively onerous and fraught with technical and experimental difficulties, the access of high-quality dynamical models is often limited. The last ten years has seen a tremendous amount of research activity on the development of model free control techniques. One leading technique is extremum-seeking control (ESC). This technique has been applied extensively in many application areas such as biomedical engineering, aerospace engineering, automotive, biotechnology and process control. In this presentation, we seek to review some of the new developments on the generalization of extremum seeking control as a data-driven controller design technique. It is shown how one can apply this technique to design reliable control systems that require only limited knowledge of the system dynamics. Several applications are presented to demonstrate the versatility of this technique.