Extremum Seeking-based Iterative Learning Linear MPC
arXiv:1409.2123 · doi:10.1109/CCA.2014.6981582
Abstract
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 on-line the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.
To appear at the IEEE MSC 2014