  A control affine nonlinear discrete time system is considerd with mached and
bounded state dependent uncertainties. Since the structure of uncertainties is
not known, a deep learning based adaptive mechanism is utilized to encounter
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. In addition, the proposed approach is applicable to multi-agent systems
with non-interacting agents and a centralized controller.
