Controlling nonlinear dynamical systems into arbitrary states using machine learning

• URL: http://arxiv.org/abs/2102.12969v1
• Date: Tue, 23 Feb 2021 16:58:26 GMT
• Title: Controlling nonlinear dynamical systems into arbitrary states using machine learning
• Authors: Alexander Haluszczynski, Christoph R\"ath
• Abstract summary: We propose a novel and fully data driven control scheme which relies on machine learning (ML) Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state. Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.
• Score: 77.34726150561087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel and fully data driven control scheme which relies on machine learning (ML). Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state. We outline our approach using the examples of the Lorenz and the R\"ossler system and show how these systems can very accurately be brought not only to periodic but also to e.g. intermittent and different chaotic behavior. Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.

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