Open AccessQuantised MPC for LPV systems by using new Lyapunov–Krasovskii functional
Author(s) -
Lee Sangmoon,
Kwon Ohmin
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0597
Subject(s) - control theory (sociology) , model predictive control , piecewise , bounded function , mathematics , stability (learning theory) , constant (computer programming) , mathematical optimization , computer science , control (management) , mathematical analysis , artificial intelligence , machine learning , programming language
This study deals with the problem of sampled‐data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled‐data and quantisation of the control input, the closed‐loop system is modelled as a continuous‐time impulsive dynamic model with sector non‐linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results.