Extremum Seeking-based Iterative Learning Model Predictive Control

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 [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.