Local Koopman Operators for Data-Driven Control of Robotic Systems

Giorgos Mamakoukas, Maria Castaño, Xiaobo Tan, Todd Murphey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XV
EditorsAntonio Bicchi, Hadas Kress-Gazit, Seth Hutchinson
PublisherMIT Press Journals
ISBN (Print)9780992374754
StatePublished - 2019
Event15th Robotics: Science and Systems, RSS 2019 - Freiburg im Breisgau, Germany
Duration: Jun 22 2019Jun 26 2019

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference15th Robotics: Science and Systems, RSS 2019
CityFreiburg im Breisgau

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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