Extremum Seeking-based Iterative Learning Linear MPC
Mouhacine Benosman, Stefano Di Cairano, Avishai Weiss
TL;DR
This paper introduces an iterative extremum seeking-based learning model predictive control algorithm for linear systems with parametric uncertainties, enabling online adaptation and improved control performance.
Contribution
It presents a novel MES-based adaptive MPC method that learns uncertain parameters online, enhancing control accuracy for linear systems with uncertainties.
Findings
Effective parameter learning demonstrated on a DC servo motor
Improved control performance through online adaptation
Validation of the algorithm's effectiveness in simulation
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.
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