  Soft robots are challenging to model due in large part to the nonlinear
properties of soft materials. Fortunately, this softness makes it possible to
safely observe their behavior under random control inputs, making them amenable
to large-scale data collection and system identification. This paper implements
and evaluates a system identification method based on Koopman operator theory
in which models of nonlinear dynamical systems are constructed via linear
regression of observed data by exploiting the fact that every nonlinear system
has a linear representation in the infinite-dimensional space of real-valued
functions called observables. The approach does not suffer from some of the
shortcomings of other nonlinear system identification methods, which typically
require the manual tuning of training parameters and have limited convergence
guarantees. A dynamic model of a pneumatic soft robot arm is constructed via
this method, and used to predict the behavior of the real system. The total
normalized-root-mean-square error (NRMSE) of its predictions is lower than that
of several other identified models including a neural network, NLARX, nonlinear
Hammerstein-Wiener, and linear state space model.
