In this paper we propose a constrained guaranteed cost robust model predictive controller (GCMPC) for uncertain discrete time systems. This controller was developed based on a quadratic cost functional and guarantee robustness with respect to quadratically bound uncertainties. Such a class of problems is currently intractable by Min-Max Robust Model Predictive Controllers without polytopic approximations of the uncertainties. The proposed technique is computationally more efficient then an enumeration-based approach and requires only a Quadratically Constrained Quadratic Problem (QCQP) optimization, whereas LMI-based GCMPC approaches require a Semi-Definite Programming (SDP) optimization.
@article{arxiv.1606.03437,
title = {Guaranteed Cost Approach to Robust Model Predictive Control of Uncertain Linear Systems},
author = {Carlos M. Massera and Marco H. Terra and Denis F. Wolf},
journal= {arXiv preprint arXiv:1606.03437},
year = {2018}
}