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AuthorChitraganti, Shaikshavali
AuthorToth, Roland
AuthorMeskin, Nader
AuthorMohammadpour, Javad
Available date2020-12-02T07:03:52Z
Publication Date2017
Publication NameIET Control Theory and Applications
ResourceScopus
ISSN17518644
URIhttp://dx.doi.org/10.1049/iet-cta.2016.0629
URIhttp://hdl.handle.net/10576/17172
AbstractThis study addresses a stochastic model predictive tracking problem for linear parameter-varying (LPV) systems described by affine parameter-dependent state-space representations and additive stochastic uncertainties. The reference trajectory is considered as a piecewise constant signal and assumed to be known at all time instants. To obtain prediction equations, the scheduling signal is usually assumed to be constant or its variation is assumed to belong to a convex set. In this study, the underlying scheduling signal is given a stochastic description during the prediction horizon, which aims to overcome the shortcomings of the two former characterisations, namely restrictiveness and conservativeness. Hence, the overall LPV system dynamics consists of additive and multiplicative noise terms up to second order. Due to the presence of stochastic disturbances, probabilistic state constraints are considered. Since the disturbances make the computation of prediction dynamics difficult, augmented state prediction dynamics are considered, by which, feasibility of probabilistic constraints and closed-loop stability are addressed. The overall approach is illustrated using a tank system model. 1 The Institution of Engineering and Technology 2017.
Languageen
PublisherInstitution of Engineering and Technology
SubjectModel Predictive Control
Linear Parameter-varying Systems
Feedback Law
TitleStochastic model predictive tracking of piecewise constant references for LPV systems
TypeArticle
Pagination1862-1872
Issue Number12
Volume Number11
dc.accessType Abstract Only


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