Extremum seeking-based iterative learning linear MPC

In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-variable extremum seeking (MES)-based learning MPC algorithm to learn online the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.