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arXiv · 1512.02627

Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)

Anantharaman Subbaraman·Mouhacine Benosman
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Abstract

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.

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BibTeXRIS

Anantharaman Subbaraman, Mouhacine Benosman. 2015-12-08. Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC). https://arxiv.org/abs/1512.02627

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