Adaptive model predictive control using max-plus-linear input-output models


Reference:

T.J.J. van den Boom, B. De Schutter, G. Schullerus, and V. Krebs, "Adaptive model predictive control using max-plus-linear input-output models," Proceedings of the 2003 American Control Conference, Denver, Colorado, pp. 933-938, June 2003.

Abstract:

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is "linear" in the max-plus algebra. In our previous work we have considered MPC for the time-invariant case. In this paper we consider an adaptive scheme for the time-varying case, based on parameter estimation of input-output models. In a simulation example we show that the combined parameter-estimation/MPC algorithm gives a good closed-loop behaviour.

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Bibtex entry:

@inproceedings{vanDeS:02-021,
author={T.J.J. van den Boom and B. {D}e Schutter and G. Schullerus and V. Krebs},
title={Adaptive model predictive control using max-plus-linear input-output models},
booktitle={Proceedings of the 2003 American Control Conference},
address={Denver, Colorado},
pages={933--938},
month=jun,
year={2003}
}


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