Complexity reduction in MPC for stochastic max-plus-linear systems by variability expansion

T.J.J. Boom, van den, B. Schutter, de, B.F. Heidergott

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)


Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Previously, 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 perturbations-free case and for the case with noise and/or modeling errors in a bounded or stochastic setting. In this paper we consider a method to reduce the computational complexity of the resulting optimization problem, based on variability expansion. We show that the computational load is reduced if we decrease the level of 'randomness' in the system.
Original languageEnglish
Title of host publicationProceedings 41st IEEE Conference on Decision and Control (Las Vegas NV, USA, December 10-13, 2002)
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)0-7803-7518-5
Publication statusPublished - 2002


Dive into the research topics of 'Complexity reduction in MPC for stochastic max-plus-linear systems by variability expansion'. Together they form a unique fingerprint.

Cite this