Abstract
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 language | English (US) |
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Title of host publication | Robotics |
Subtitle of host publication | Science and Systems XV |
Editors | Antonio Bicchi, Hadas Kress-Gazit, Seth Hutchinson |
Publisher | MIT Press Journals |
ISBN (Print) | 9780992374754 |
DOIs | |
State | Published - 2019 |
Event | 15th Robotics: Science and Systems, RSS 2019 - Freiburg im Breisgau, Germany Duration: Jun 22 2019 → Jun 26 2019 |
Publication series
Name | Robotics: Science and Systems |
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ISSN (Electronic) | 2330-765X |
Conference
Conference | 15th Robotics: Science and Systems, RSS 2019 |
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Country/Territory | Germany |
City | Freiburg im Breisgau |
Period | 6/22/19 → 6/26/19 |
Funding
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.
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
- Electrical and Electronic Engineering