PRG Seminar Series
on
Robotics And Computer Vision
Geometric Mechanics and Controls on Nonlinear Manifolds for Complex Dynamic Systems
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Geometric Mechanics and Controls on Nonlinear Manifolds for Complex Dynamic Systems
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Tuesday, October 25, 2019
10:30 AM
J.M. Patterson 2121
ABSTRACT
Many interesting dynamic systems in science and engineering evolve on a nonlinear, or curved space that cannot be globally identified with a linear space. Such nonlinear spaces are referred to as manifolds, and they appear in various mechanical systems, such as a planar pendulum or a complex robotic system. However, geometric structures of a nonlinear manifold have not been carefully incorporated into control system engineering. Conventional nonlinear control systems based on local coordinates of a manifold suffer from singularities, ambiguities, and complexities, thereby severely restricting control performance. This talk summarizes recent advances in geometric approaches for four major topics in nonlinear dynamics and controls, namely computational mechanics, optimization, feedback control, and estimation. It is shown that aggressive maneuvers of complex dynamic systems can be achieved in an intrinsic and elegant fashion, by constructing control systems directly on a manifold. The desirable properties of geometric mechanics and controls are illustrated by both computational and experimental results of several aerospace and robotic systems, including binary asteroid, formation reconfiguration of satellites, tethered spacecraft, and quadrotor unmanned aerial vehicles.
ABOUT THE SPEAKER
Dr. Taeyoung Lee is Associate Professor of the Department of Mechanical and Aerospace Engineering at the George Washington University, Washington DC. He received his doctoral degree in Aerospace Engineering, and his master's degree in Mathematics at the University of Michigan in 2008. His research interests include geometric mechanics, geometric control, optimization, estimation, and uncertainty propagation with applications to aerospace systems. His research has been supported by AFOSR, NASA, NRL, NSF, and ONR.