Stochastic model predictive control for LPV systems

S. Chitraganti, R. Toth, N. Meskin, J. Mohammadpour

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

3 Citations (Scopus)
1 Downloads (Pure)


This paper considers a stochastic model predictive control of linear parameter-varying (LPV) systems described by affine parameter dependent state-space representations with additive stochastic uncertainties and probabilistic state constraints. In computing the prediction dynamics for LPV systems, the scheduling signal is given a stochastic description during the prediction horizon, which aims to overcome the shortcomings of the existing approaches where the scheduling signal is assumed to be constant or allowed to vary in a convex set. The above representation leads to LPV system dynamics consisting of additive and multiplicative uncertain stochastic terms up to second order. The prediction dynamics are reposed in an augmented form, which facilitates the feasibility of probabilistic constraints and closed-loop stability in the presence of stochastic uncertainties.

Original languageEnglish
Title of host publication2017 American Control Conference, ACC 2017, 24-26 May 2017, Seattle, Washington
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5090-5992-8
ISBN (Print)978-1-5090-4583-9
Publication statusPublished - 29 Jun 2017
Event2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, United States
Duration: 24 May 201726 May 2017


Conference2017 American Control Conference (ACC 2017)
Abbreviated titleACC 2017
Country/TerritoryUnited States
Internet address


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