TY - GEN
T1 - Local Koopman Operators for Data-Driven Control of Robotic Systems
AU - Mamakoukas, Giorgos
AU - Castaño, Maria
AU - Tan, Xiaobo
AU - Murphey, Todd
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation (IIS-1717951, IIS-1715714, DGE1424871). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2019, Robotics: Science and Systems. All rights reserved.
PY - 2019
Y1 - 2019
N2 - This paper presents a data-driven methodology for linear embedding of nonlinear systems. Utilizing structural knowledge of general nonlinear dynamics, the authors exploit the Koopman operator to develop a systematic, data-driven approach for constructing a linear representation in terms of higher order derivatives of the underlying nonlinear dynamics. With the linear representation, the nonlinear system is then controlled with an LQR feedback policy, the gains of which need to be calculated only once. As a result, the approach enables fast control synthesis. We demonstrate the efficacy of the approach with simulations and experimental results on the modeling and control of a tail-actuated robotic fish and show that the proposed policy is comparable to backstepping control. To the best of our knowledge, this is the first experimental validation of Koopman-based LQR control.
AB - This paper presents a data-driven methodology for linear embedding of nonlinear systems. Utilizing structural knowledge of general nonlinear dynamics, the authors exploit the Koopman operator to develop a systematic, data-driven approach for constructing a linear representation in terms of higher order derivatives of the underlying nonlinear dynamics. With the linear representation, the nonlinear system is then controlled with an LQR feedback policy, the gains of which need to be calculated only once. As a result, the approach enables fast control synthesis. We demonstrate the efficacy of the approach with simulations and experimental results on the modeling and control of a tail-actuated robotic fish and show that the proposed policy is comparable to backstepping control. To the best of our knowledge, this is the first experimental validation of Koopman-based LQR control.
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U2 - 10.15607/RSS.2019.XV.054
DO - 10.15607/RSS.2019.XV.054
M3 - Conference contribution
AN - SCOPUS:85112525985
SN - 9780992374754
T3 - Robotics: Science and Systems
BT - Robotics
A2 - Bicchi, Antonio
A2 - Kress-Gazit, Hadas
A2 - Hutchinson, Seth
PB - MIT Press Journals
T2 - 15th Robotics: Science and Systems, RSS 2019
Y2 - 22 June 2019 through 26 June 2019
ER -