A model predictive control approach for stochastic networked control systems

D. Bernardini, M.C.F. Donkers, A. Bemporad, W.P.M.H. Heemels

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    9 Citations (Scopus)
    2 Downloads (Pure)


    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 timevarying 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.
    Original languageEnglish
    Title of host publicationProceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems
    Place of PublicationFrance, Annecy
    Publication statusPublished - 2010


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