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.


Downloads:
 * Corresponding technical report: pdf file (162 KB)
      Note: More information on the pdf file format mentioned above can be found here.


Bibtex entry:

@inproceedings{vanDeS:02-021,
        author={T.J.J. {van den Boom} and B. {De 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}
        }



Go to the publications overview page.


This page is maintained by Bart De Schutter. Last update: December 15, 2015.