Specifically, the Koopman operator propagates a nonlinear system in a linear manner without loss of accuracy by evolving functions of the states, termed ...
Abstract—This paper presents a data-driven methodology for linear embedding of nonlinear systems. Utilizing structural knowledge of general nonlinear ...
Nov 2, 2019 · This paper presents a data-driven methodology for the linear embedding of nonlinear systems. Utilizing structural knowledge of general ...
The authors exploit the Koopman operator to develop a systematic, data-driven approach for constructing a linear representation in terms of higher order ...
Abstract: This paper presents a data-driven methodology for linear embedding of nonlinear systems. Utilizing structural knowledge of general nonlinear dynamics, ...
May 20, 2019 · Robotics: Science and Systems 2019. This paper can be found at ...
Jun 1, 2019 · This paper presents a data-driven methodology for linear embedding of nonlinear systems. Utilizing structural knowledge of general nonlinear ...
Publications. Active learning of dynamics for data-driven control using Koopman operators ... Local Koopman operators for data-driven control of robotic systems
Data-driven Koopman operators for model-based shared control of ...
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Jun 10, 2020 · We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines.
Our approach relies on the Koopman operator, which is a linear but infinite-dimensional operator lifting the nonlinear system to a higher-dimensional space.