Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for control. Brunton, S. L., Brunton, B. W., Proctor, J. L., & Kutz, J. N. arXiv:1510.03007 [math], October, 2015. arXiv: 1510.03007![Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for control [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to a subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves observable functions on the state-space of a dynamical system [Koopman 1931, PNAS]. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear observations of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems [Williams et al. 2015, JNLS]. It remains an open challenge how to choose the right nonlinear observable functions to form a subspace where it is possible to obtain efficient linear reduced-order models. Here, we investigate the choice of observable functions for Koopman analysis. First, we note that to obtain a linear Koopman system that advances the original states, it is helpful to include these states in the observable subspace, as in DMD. We then categorize dynamical systems by whether or not there exists a Koopman-invariant subspace that includes the state variables as observables. In particular, we note that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not topologically conjugate to a finite-dimensional linear system; this is illustrated using the logistic map. Second, we present a data-driven strategy to identify relevant observable functions for Koopman analysis. We use a new algorithm that determines terms in a dynamical system by sparse regression of the data in a nonlinear function space [Brunton et al. 2015, arxiv]; we also show how this algorithm is related to DMD. Finally, we design Koopman operator optimal control laws for nonlinear systems using techniques from linear optimal control.
@article{brunton_koopman_2015,
title = {Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for control},
url = {http://arxiv.org/abs/1510.03007},
abstract = {In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to a subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves observable functions on the state-space of a dynamical system [Koopman 1931, PNAS]. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear observations of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems [Williams et al. 2015, JNLS]. It remains an open challenge how to choose the right nonlinear observable functions to form a subspace where it is possible to obtain efficient linear reduced-order models. Here, we investigate the choice of observable functions for Koopman analysis. First, we note that to obtain a linear Koopman system that advances the original states, it is helpful to include these states in the observable subspace, as in DMD. We then categorize dynamical systems by whether or not there exists a Koopman-invariant subspace that includes the state variables as observables. In particular, we note that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not topologically conjugate to a finite-dimensional linear system; this is illustrated using the logistic map. Second, we present a data-driven strategy to identify relevant observable functions for Koopman analysis. We use a new algorithm that determines terms in a dynamical system by sparse regression of the data in a nonlinear function space [Brunton et al. 2015, arxiv]; we also show how this algorithm is related to DMD. Finally, we design Koopman operator optimal control laws for nonlinear systems using techniques from linear optimal control.},
urldate = {2015-10-14TZ},
journal = {arXiv:1510.03007 [math]},
author = {Brunton, Steven L. and Brunton, Bingni W. and Proctor, Joshua L. and Kutz, J. Nathan},
month = oct,
year = {2015},
note = {arXiv: 1510.03007},
keywords = {Mathematics - Dynamical Systems, \_tablet}
}
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