A Model Predictive Control Approach for Stochastic Networked Control Systems

In this paper we present a stochastic model predictive control (SMPC) approach for networked control systems (NCSs) that are subject to time-varying sampling intervals and time-varying transmission delays. These network-induced uncertain parameters are assumed to be described by random processes, ha...

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Published inIFAC Proceedings Volumes Vol. 43; no. 19; pp. 7 - 12
Main Authors Bernardini, D., Donkers, M.C.F., Bemporad, A., Heemels, W.P.M.H.
Format Journal Article
LanguageEnglish
Published 2010
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ISSN1474-6670
DOI10.3182/20100913-2-FR-4014.00007

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Abstract In this paper we present a stochastic model predictive control (SMPC) approach for networked control systems (NCSs) that are subject to time-varying sampling intervals and time-varying transmission delays. These network-induced uncertain parameters are assumed to be described by random processes, having a bounded support and an arbitrary continuous probability density function. Assuming that the controlled plant can be modeled as a linear system, we present a SMPC formulation based on scenario enumeration and quadratic programming that optimizes a stochastic performance index and provides closed-loop stability in the mean-square sense. Simulation results are shown to demonstrate the performance of the proposed approach.
AbstractList In this paper we present a stochastic model predictive control (SMPC) approach for networked control systems (NCSs) that are subject to time-varying sampling intervals and time-varying transmission delays. These network-induced uncertain parameters are assumed to be described by random processes, having a bounded support and an arbitrary continuous probability density function. Assuming that the controlled plant can be modeled as a linear system, we present a SMPC formulation based on scenario enumeration and quadratic programming that optimizes a stochastic performance index and provides closed-loop stability in the mean-square sense. Simulation results are shown to demonstrate the performance of the proposed approach.
Author Heemels, W.P.M.H.
Bernardini, D.
Bemporad, A.
Donkers, M.C.F.
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CitedBy_id crossref_primary_10_1109_TAC_2017_2765740
crossref_primary_10_1007_s10846_020_01267_0
crossref_primary_10_1109_MCS_2016_2602087
Cites_doi 10.1080/07362998308809005
10.1109/37.898794
10.1109/TAC.2004.834107
10.1109/TAC.2005.844177
10.1109/TAC.2009.2015543
10.1016/j.automatica.2006.07.006
10.1109/JPROC.2006.887291
10.1109/JPROC.2006.887288
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