کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
698517 890412 2007 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Complexity reduction in MPC for stochastic max-plus-linear discrete event systems by variability expansion
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Complexity reduction in MPC for stochastic max-plus-linear discrete event systems by variability expansion
چکیده انگلیسی

Model predictive control (MPC) is a popular controller design technique in the process industry. Recently, MPC has been extended to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In this context both the perturbations-free case and for the case with noise and/or modeling errors in a bounded or stochastic setting have been considered. In each of these cases an optimization problem has to be solved on-line at each event step in order to determine the MPC input. This paper considers a method to reduce the computational complexity of this optimization problem, based on variability expansion. In particular, it is shown that the computational load is reduced if one decreases the level of “randomness” in the system.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 43, Issue 6, June 2007, Pages 1058–1063
نویسندگان
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