  In this paper, we study a tracking control problem for linear time-invariant
systems, with model parametric uncertainties, under input and states
constraints. We apply the idea of modular design introduced in Benosman et al.
2014, to solve this problem in the model predictive control (MPC) framework. We
propose to design an MPC with input-to-state stability (ISS) guarantee, and
complement it with an extremum seeking (ES) algorithm to iteratively learn the
model uncertainties. The obtained MPC algorithms can be classified as iterative
learning control (ILC)-MPC.
