AccScience Publishing / IJOCTA / Volume 3 / Issue 2 / DOI: 10.11121/ijocta.01.2013.00140
OPTIMIZATION & APPLICATIONS

Model predictive control-based scheduler for repetitive discrete event systems with capacity constraints

Hiroyuki Goto1*
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1 Department of Industrial and System Engineering, Hosei University, Japa
Received: 19 September 2012 | Published online: 4 April 2013
© 2013 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
A model predictive control-based scheduler for a class of discrete event systems is designed and developed. We focus on repetitive, multiple-input, multiple-output, and directed acyclic graph structured systems on which capacity constraints can be imposed. The target system’s behaviour is described by linear equations in max-plus algebra, referred to as state-space representation. Assuming that the system’s performance can be improved by paying additional cost, we adjust the system parameters and determine control inputs for which the reference output signals can be observed. The main contribution of this research is twofold, 1: For systems with capacity constraints, we derived an output prediction equation as functions of adjustable variables in a recursive form, 2: Regarding the construct for the system’s representation, we improved the structure to accomplish general operations which are essential for adjusting the system parameters. The result of numerical simulation in a later section demonstrates the effectiveness of the developed controller.
Keywords
Model predictive control
max-plus algebra
capacity constraint
adjustable parameter
internal representation
Conflict of interest
The authors declare they have no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing