  Underwater Unmanned Vehicles (UUVs) have to constantly compensate for the
external disturbing forces acting on their body. Adaptive Control theory is
commonly used there to grant the control law some flexibility in its response
to process variation. Today, learning-based (LB) adaptive methods are leading
the field where model-based control structures are combined with deep
model-free learning algorithms. This work proposes experiments and metrics to
empirically study the stability of such a controller. We perform this stability
analysis on a LB adaptive control system whose adaptive parameters are
determined using a Cross-Entropy Deep Learning method.
