NDLI: Model predictive control for stochastic max-min-plus-scaling systems

Content Provider	IEEE Xplore Digital Library
Author	Farahani, S.S. van den Boom, T. De Schutter, B.
Copyright Year	2011
Description	Author affiliation: Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD, The Netherlands (Farahani, S.S.; van den Boom, T.; De Schutter, B.)
Abstract	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. Further, Model Predictive Control (MPC), which is one of the most widely used advanced control design methods in the process industry due to its ability to handle constraints on both inputs and outputs, has already been extended to both deterministic and stochastic MMPS systems. However, in order to compute an MPC controller for a general MMPS system, a nonlinear, nonconvex optimization problem has to be solved. In addition, for stochastic MMPS systems, the problem is computationally highly complex since the cost function is defined as the expected value of an MMPS function and its evaluation leads to a complex numerical integration. The aim of this paper is to decrease this computational complexity by applying an approximation method that is based on the raw moments of a random variable, to a stochastic MMPS system with a Gaussian noise. In this way, the problem can be transformed into a sequence of convex optimization problems, providing that linear or convex MPC input constraints are considered.
Starting Page	391
Ending Page	396
File Size	180915
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160288
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Convex functions Approximation methods Upper bound Vectors Random variables Optimization
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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