Learning Linear Representations of Nonlinear Dynamics Using Deep Learning

We propose a novel deep learning framework to discover a transformation of a nonlinear dynamical system to an equivalent higher dimensional linear representation. We demonstrate that the resulting learned linear representation accurately captures the dynamics of the original system for a wider range of conditions than standard linearization. As a result of this, we show that the learned linear model can subsequently be used for the successful control of the original system. We demonstrate this by applying the proposed framework to two examples; one from the literature and another more complex example in the form of a Continuous Stirred Tank Reactor (CSTR). Copyright (C) 2022 The Authors.