  This paper presents a deep learning based model predictive control algorithm
for control affine nonlinear discrete time systems with matched and bounded
state-dependent uncertainties of unknown structure. Since the structure of
uncertainties is not known, a deep neural network (DNN) is employed to
approximate the disturbances. In order to avoid any unwanted behavior during
the learning phase, a tube based model predictive controller is employed, which
ensures satisfaction of constraints and input-to-state stability of the
closed-loop states.
