Learning Linear Representations of Nonlinear Dynamics Using Deep Learning

TL;DR

This paper introduces a deep learning framework that transforms nonlinear dynamical systems into higher-dimensional linear representations, enabling more effective control and analysis beyond traditional linearization methods.

Contribution

The authors propose a novel deep learning approach to find linear representations of nonlinear systems, improving control accuracy over a wider range of conditions.

Findings

Learned linear models accurately capture system dynamics.

Framework successfully applied to complex systems like CSTR.

Enhanced control performance demonstrated compared to standard methods.

Abstract

The vast majority of systems of practical interest are characterised by nonlinear dynamics. This renders the control and optimization of such systems a complex task due to their nonlinear behaviour. Additionally, standard methods such as linearizing around a fixed point may not be an effective strategy for many systems, thus requiring an alternative approach. For this reason, we propose a new 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 a more complex example in the form of a Continuous Stirred Tank Reactor (CSTR).