M
Myungsoo Jun
Researcher at National Renewable Energy Laboratory
Publications - 32
Citations - 747
Myungsoo Jun is an academic researcher from National Renewable Energy Laboratory. The author has contributed to research in topics: Battery (electricity) & Electric vehicle. The author has an hindex of 13, co-authored 30 publications receiving 665 citations. Previous affiliations of Myungsoo Jun include University of Southern California & Massachusetts Institute of Technology.
Papers
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Book ChapterDOI
Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments
Myungsoo Jun,Raffaello D'Andrea +1 more
TL;DR: A path planning algorithm based on a map of the probability of threats, which can be built from a priori surveillance data is proposed, and simulation results are provided.
Proceedings ArticleDOI
State estimation of an autonomous helicopter using Kalman filtering
TL;DR: This work presents a technique to accurately estimate the state of a robot helicopter using a combination of gyroscopes, accelerometers, inclinometers and GPS and describes the larger context in which this work is embedded, namely the design and implementation of an autonomous robot helicopter.
Proceedings ArticleDOI
Automatic PID tuning: an application of unfalsified control
Myungsoo Jun,Michael G. Safonov +1 more
TL;DR: This paper gives detailed procedures for using unfalsified control theory for real-time PID controller parameter tuning and adaptation and makes PID gain selection possible by just using observed data.
Proceedings ArticleDOI
Piecewise quadratic Lyapunov functions for piecewise affine time-delay systems
TL;DR: In this article, the authors investigate some particular classes of hybrid systems subject to a class of time delays; the time delays can be constant or time varying, and they present the corresponding classes of piecewise continuous Lyapunov functions.
Journal ArticleDOI
A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations
Zonggen Yi,Don Scoffield,John Smart,Andrew Meintz,Myungsoo Jun,Manish Mohanpurkar,Anudeep Medam +6 more
TL;DR: A highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations and a two-stage hierarchical optimization routine that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process.