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

TL;DR

This paper introduces an iterative learning model predictive control approach combining extremum seeking to handle uncertainties in linear systems with constraints, ensuring stability and improved model accuracy over iterations.

Contribution

It proposes a novel ESILC-MPC framework that integrates extremum seeking with MPC to adaptively learn uncertainties while maintaining stability.

Findings

MPC with ISS guarantees for uncertain systems

Integration of extremum seeking for model uncertainty adaptation

Enhanced tracking performance through iterative learning

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.