TY - JOUR
T1 - On optimization of stochastic max–min-plus-scaling systems
T2 - An approximation approach
AU - Farahani, Samira S.
AU - van den Boom, Ton
AU - De Schutter, Bart
PY - 2017
Y1 - 2017
N2 - A large class of discrete-event and hybrid systems can be described by a max–min-plus-scaling (MMPS) model, i.e., a model in which the main operations are maximization, minimization, addition, and scalar multiplication. Accordingly, optimization of MMPS systems appears in different problems defined for discrete-event and hybrid systems. For a stochastic MMPS system, this optimization problem is computationally highly demanding as often numerical integration has to be used to compute the objective function. The aim of this paper is to decrease such computational complexity by applying an approximation method that is based on the moments of a random variable and that can be computed analytically.
AB - A large class of discrete-event and hybrid systems can be described by a max–min-plus-scaling (MMPS) model, i.e., a model in which the main operations are maximization, minimization, addition, and scalar multiplication. Accordingly, optimization of MMPS systems appears in different problems defined for discrete-event and hybrid systems. For a stochastic MMPS system, this optimization problem is computationally highly demanding as often numerical integration has to be used to compute the objective function. The aim of this paper is to decrease such computational complexity by applying an approximation method that is based on the moments of a random variable and that can be computed analytically.
KW - Discrete event systems
KW - Max–min-plus-scaling systems
KW - Moments
KW - Optimization
KW - Stochastic disturbance
UR - http://www.scopus.com/inward/record.url?scp=85019983601&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2017.05.001
DO - 10.1016/j.automatica.2017.05.001
M3 - Article
AN - SCOPUS:85019983601
SN - 0005-1098
VL - 83
SP - 20
EP - 27
JO - Automatica
JF - Automatica
ER -