Learning Model Predictive Control for Periodic Repetitive Tasks
Systems and Control
2020-04-20 v4 Systems and Control
Abstract
We propose a reference-free learning model predictive controller for periodic repetitive tasks. We consider a problem in which dynamics, constraints and stage cost are periodically time-varying. The controller uses the closed-loop data to construct a time-varying terminal set and a time-varying terminal cost. We show that the proposed strategy in closed-loop with linear and nonlinear systems guarantees recursive constraints satisfaction, non-increasing open-loop cost, and that the open-loop and closed-loop cost are the same at convergence. Simulations are presented for different repetitive tasks, both for linear and nonlinear systems.
Cite
@article{arxiv.1911.07535,
title = {Learning Model Predictive Control for Periodic Repetitive Tasks},
author = {Nicola Scianca and Ugo Rosolia and Francesco Borrelli},
journal= {arXiv preprint arXiv:1911.07535},
year = {2020}
}
Comments
2020 European Control Conference, Saint Petersburg, Russia. Extended version of the conference paper