  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).
