Speaker Prof. Gyunghoon Park
(School of Electrical and Computer Engineering, University of Seoul)
Date｜Time Tuesday, December 30, 2025 | 9:00 AM
Place Room 316-1, Building 133
Abstract
In this second part of a tutorial series, we discuss the important role that Lagrangian mechanics has played in the development of control engineering. We begin by reviewing several fundamental concepts from Lagrangian mechanics, including symmetry, cyclic coordinates, and the connection between the Lagrangian and Hamiltonian formulations. We then revisit classical optimal control theory, where the notion of a Lagrangian appears in a different but closely related form. As a more direct application, we introduce basic results from the theory of controlled Lagrangians, which has been recognized as an effective control framework for certain classes of mechanical systems. In addition, we briefly discuss how Lagrangian formulations have been incorporated into learning-based algorithms for modeling and understanding physical systems. Finally, we present a recent result by the speaker in which a disturbance observer is reformulated from a Lagrangian-mechanics perspective. Overall, this tutorial aims to highlight both the historical significance and the continuing potential of Lagrangian and Lagrangian mechanics from a control-theoretic viewpoint.
Biography
Gyunghoon Park received the B.S. degree from the School of Electrical and Computer Engineering, Sungkyunkwan University, in 2011, and the M.S. and Ph.D. degrees from the School of Electrical Engineering and Computer Science, Seoul National University, in 2013 and 2018, respectively. From 2018 to 2019, he was a Postdoctoral Researcher with the Automation and System Research Institute, Seoul National University. He also held a postdoctoral research position with the Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, until 2021. Since 2021, he has been with the University of Seoul where he is currently an Associate Professor. His research interests include theory of robust/nonlinear/optimal/multi-objective control and application to robotics and mobility, as well as security of robot systems.
