  In this paper we report an experimental evaluation of three popular methods
for online system identification of unmanned surface vehicles (USVs) which were
implemented as an ensemble: certifiably stable shallow recurrent neural network
(RNN), adaptive identification (AID), and recursive least squares (RLS). The
algorithms were deployed on eight USVs for a total of 30 hours of online
estimation. During online training the loss function for the RNN was augmented
to include a cost for violating a sufficient condition for the RNN to be stable
in the sense of contraction stability. Additionally we described an efficient
method to calculate the equilibrium points of the RNN and classify the
associated stability properties about these points. We found the AID method had
lowest mean absolute error in the online prediction setting, but a weighted
ensemble had lower error in offline processing.
