  A centralized model predictive controller (MPC), which is unaware of local
uncertainties, for an affine discrete time nonlinear system is presented. The
local uncertainties are assumed to be matched, bounded and structured. In order
to encounter disturbances and to improve performance, an adaptive control
mechanism is employed locally. The proposed approach ensures input-to-state
stability of closed-loop states and convergence to the equilibrium point.
Moreover, uncertainties are learnt in terms of the given feature basis by using
adaptive control mechanism. In addition, hard constraints on state and control
are satisfied.
