  The ability to accurately model mechanical hysteretic behavior in
tendon-actuated continuum robots using deep learning approaches is a growing
area of interest. In this paper, we investigate the hysteretic response of two
types of tendon-actuated continuum robots and, ultimately, compare three types
of neural network modeling approaches with both forward and inverse kinematic
mappings: feedforward neural network (FNN), FNN with a history input buffer,
and long short-term memory (LSTM) network. We seek to determine which model
best captures temporal dependent behavior. We find that, depending on the
robot's design, choosing different kinematic inputs can alter whether
hysteresis is exhibited by the system. Furthermore, we present the results of
the model fittings, revealing that, in contrast to the standard FNN, both FNN
with a history input buffer and the LSTM model exhibit the capacity to model
historical dependence with comparable performance in capturing rate-dependent
hysteresis.
