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arXiv 2021年

作者： Antonelo, Eric Aislan Camponogara, Eduardo Seman, Laio Oriel de Souza, Eduardo Rehbein Jordanou, Jean Panaioti Hübner, Jomi Fred Department of Automation and Systems Engineering Federal University of Santa Catarina FlorianópolisCEP: 88040-900 Brazil Gradute Program in Applied Computer Science University of Vale do Itajaí Itajaí CEP: 88302-901 Brazil

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs, neither can they simulate for variable long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to control problems and able to simulate for longer-range time horizons that are not fixed beforehand, making it a very flexible framework when compared to traditional PINNs. Furthermore, this long-range time simulation of differential equations is faster than numerical methods since it relies only on signal propagation through the network, making it less computationally costly and, thus, a better alternative for simulation of models in Model Predictive Control. We showcase our proposal in the control of two nonlinear dynamic systems: The Van der Pol oscillator and the four-tank system. Copyright © 2021, The Authors. All rights reserved.

关键词： Ordinary differential equations

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