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arXiv · 1311.5250

Attractor Control Using Machine Learning

Thomas Duriez·Vladimir Parezanovic·Bernd R. Noack·Laurent Cordier·Marc Segond·Markus Abel
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Abstract

We propose a general strategy for feedback control design of complex dynamical systems exploiting the nonlinear mechanisms in a systematic unsupervised manner. These dynamical systems can have a state space of arbitrary dimension with finite number of actuators (multiple inputs) and sensors (multiple outputs). The control law maps outputs into inputs and is optimized with respect to a cost function, containing physics via the dynamical or statistical properties of the attractor to be controlled. Thus, we are capable of exploiting nonlinear mechanisms, e.g. chaos or frequency cross-talk, serving the control objective. This optimization is based on genetic programming, a branch of machine learning. This machine learning control is successfully applied to the stabilization of nonlinearly coupled oscillators and maximization of Lyapunov exponent of a forced Lorenz system. We foresee potential applications to most nonlinear multiple inputs/multiple outputs control problems, particulary in experiments.

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BibTeXRIS

Thomas Duriez, Vladimir Parezanovic, Bernd R. Noack, Laurent Cordier, Marc Segond, Markus Abel. 2013-11-20. Attractor Control Using Machine Learning. https://arxiv.org/abs/1311.5250

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