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Details for article 6 of 9 found articles
| Title: |
Model predictive control for perturbed max-plus-linear systems: a stochastic approach |
| Author: |
Boom, T. J. J. Van Den De Schutter, B. |
| Appeared in: |
International journal of control |
| Paging: | Volume 77 (2004) nr. 3 pages 302-309 |
| Year: |
2004-02-15 |
| Contents: |
Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or non-linear 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, +) algebra. In our previous work we have only considered MPC for the perturbations-free case and for the case with bounded noise and/or modelling errors. In this paper we extend these results on MPC for max-plus-linear systems to a stochastic setting. We show that under quite general conditions the resulting optimization problems turn out to be convex and can thus be solved very efficiently. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 6 of 9 found articles
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