Welcome to Jongeun's homepage.

I received my Ph.D. and M.S. degrees in Mechanical Engineering from the University of California at Berkeley in 2006 and 2002 respectively. I also received a B.S. degree in Mechanical Design and Production Engineering from Yonsei University at Seoul, Republic of Korea in 1998. I am currently an Assistant Professor with the Department of Mechanical Engineering at the Michigan State University.

My research interests include adaptive, learning, distributed and robust control, with applications to unsupervised competitive learning algorithms, self-organizing systems, distributed coordination algorithms for autonomous vehicles, multiple robust controllers and micro-electromechanical systems (MEMS). I was a finalist for the Student Best Paper Award at the 24th American Control Conference 2005.

Research Activities

Distributed learning and cooperative control of multi-agent systems: Currently, we are developing a network of collaboratively learning mobile sensing vehicles for achieving a global goal in an unknown changing environment. Mobile vehicles need to locally learn the uncertain environment collaboratively with neighboring vehicles to achieve a global goal. This research has been supported by Intramural Research Grants Program in Michigan State University. Simulation pictures.

Multiple robust controllers: We are also investigating a method to design the optimal classification of dynamics and corresponding optimal robust controllers, which is formulated as a nonconvex optimization problem. We have developed a systematic way to obtain suboptimal solutions numerically for this problem. This new technique has been used to obtain a set of track-following controllers for hard disk drives. This algorithm can be also applied to design multiple switching controllers for light vehicle dynamics subject to large changes in load configurations.

System identification: Parameter reduction techniques are being developed to simplify robust and adaptive controller design. The system is assumed to have a known structure with parametric uncertainties caused by system dynamics variation. An original set of parameters is identified by nonlinear least-squares (NLS) optimization using noisy frequency response functions. Using the property of asymptotic normality for NLS estimates, the original parameter set is re-parameterized by an affine function of smaller number of uncorrelated parameters. Obtaining uncorrelated parameters from the identified systems is the necessary pre-process for the synthesis of multiple robust controllers.

Adaptive musculoskeletal systems: Biological human and/or animal body systems change as time passes. We are studying the evolution and adaptation of body systems under various environments and configurations. We are currently investigating the development of a novel computational package which can simulate the time evolution of the whole human body structure from the given initial posture. With the successfully developed computational package, we will be able to predict the degeneration and/or improvements of the posture that consists of muscles and bones for any of particular everyday load configurations. Go to Body Adaptation and Musculoskeletal Functions (BAMF) Research Group. Motor control and low back pain: function, dysfunction, and adaptation: The goal is to apply concepts from modern control theory to evaluate the performance of the neuromuschular control in spine systems and to generate useful information for the rehabilitation.

Controls Research Laboratory

Unstable Seat Balancer Robot

Adaptive and Learning Control Laboratory (ALCL)