Model-based control using koopman operators

Ian Abraham, Gerardo De La Torre, Todd D. Murphey

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

55 Scopus citations


This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting modelbased control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-driven model for an unknown dynamical process that is useful for model-based control synthesis. Simulated results show that with increasing complexity in the choice of the basis functions, a closed-loop controller is able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore, the specification of the basis function are shown to be of importance when generating a Koopman operator for specific robotic systems. Experimental results with the Sphero SPRK robot explore the utility of the Koopman operator in a reduced state representation setting where increased complexity in the basis function improve open- and closed-loop controller performance in various terrains, including sand.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XIII, RSS 2017
EditorsNancy Amato, Siddhartha Srinivasa, Nora Ayanian, Scott Kuindersma
PublisherMIT Press Journals
ISBN (Electronic)9780992374730
StatePublished - 2017
Externally publishedYes
Event2017 Robotics: Science and Systems, RSS 2017 - Cambridge, United States
Duration: Jul 12 2017Jul 16 2017

Publication series

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


Other2017 Robotics: Science and Systems, RSS 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

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


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