Model Predictive Control for Uncertain Max-Min-Plus-Scaling Systems

Reference

I. Necoara, B. De Schutter, T. van den Boom, and H. Hellendoorn, "Model Predictive Control for Uncertain Max-Min-Plus-Scaling Systems," International Journal of Control, vol. 81, no. 5, pp. 701-713, May 2008.

Abstract

In this paper we extend the classical min-max model predictive control framework to a class of uncertain discrete event systems that can be modeled using the operations maximization, minimization, addition and scalar multiplication, and that we call max-min-plus-scaling (MMPS) systems. Provided that the stage cost is an MMPS expression and considering only linear input constraints then the open-loop min-max model predictive control problem for MMPS systems can be transformed into a sequence of linear programming problems. Hence, the min-max model predictive control problem for MMPS systems can be solved efficiently, despite the fact that the system is nonlinear. A min-max feedback model predictive control approach using disturbance feedback policies is also presented, which leads to improved performance compared to the open-loop approach.

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Bibtex entry

@article{NecDeS:06-035,
author={I. Necoara and B. {D}e Schutter and T. van den Boom and H. Hellendoorn},
title={Model Predictive Control for Uncertain Max-Min-Plus-Scaling Systems},
journal={International Journal of Control},
volume={81},
number={5},
pages={701--713},
month=may,
year={2008},
doi={10.1080/00207170601094404}
}

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