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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