Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/88064
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Type: Conference paper
Title: Predictive control of convex polyhedron LPV systems with Markov jumping parameters
Author: Yin, Y.
Liu, F.
Shi, P.
Karimi, H.
Citation: Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012, 2012 / pp.603-608
Publisher: IEEE
Publisher Place: USA
Issue Date: 2012
ISBN: 9781457720734
Conference Name: 24th Chinese Control and Decision Conference (CCDC) (23 May 2012 - 25 May 2012 : Taiyuan, China)
Statement of
Responsibility: 
Yin Yanyan, Liu Fei, Shi Peng, Karimi Hamid Reza
Abstract: The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon, stability of the system is guaranteed by searching a state feedback control law. Finally, receding horizon predictive controller is designed in terms of linear matrix inequality for such system. Simulation example shows the validity of this method.
Keywords: Predictive control; convex polyhedron; linear parameter varying systems; Markov jumping parameters
Rights: ©2012 IEEE
RMID: 0020128396
DOI: 10.1109/CCDC.2012.6244093
Appears in Collections:Electrical and Electronic Engineering publications

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