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An approach for Model Predictive Control of mixed integer-input linear systems based on convex relaxations

Author(s):

M. Schmitt, R. Vujanic, J. Warrington, M. Morari
Conference/Journal:

IEEE Conference on Decision and Control, Florence, Italy
Abstract:

Model predictive control (MPC) of hybrid systems (possessing mixed discrete and continuous states and/or inputs) usually requires the online solution of a mixed integer optimization problem. Such problems must typically be solved using methods whose worst-case complexity is exponential in the number of binary variables. In this paper we present an approximate MPC method for systems with binary or mixed integer inputs and continuous states. We relax the integer constraints in the finite horizon optimal control problem and then apply a mapping function to the inputs such that they satisfy the required integer constraints. In order to guarantee that state constraints are satisfied under the new input sequence, additional constraints are added to the relaxed problem using robust MPC principles. We demonstrate the approach on a DC-DC buck converter.

Year:

2013
Type of Publication:

(01)Article
Supervisor:

M. Morari

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% Autogenerated BibTeX entry
@InProceedings { SchEtal:2013:IFA_4509,
    author={M. Schmitt and R. Vujanic and J. Warrington and M. Morari},
    title={{An approach for Model Predictive Control of mixed
	  integer-input linear systems based on convex relaxations}},
    booktitle={IEEE Conference on Decision and Control},
    pages={},
    year={2013},
    address={Florence, Italy},
    month=dec,
    url={https://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=4509}
}
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