Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/103569
Type: Artigo de evento
Title: Constrained Model Predictive Control Of Jump Linear Systems With Noise And Non-observed Markov State
Author: Vargas A.N.
Furloni W.
Do Val J.B.R.
Abstract: This paper presents a variational method to the solution of the model predictive control (MPC) of discrete-time Markov jump linear systems (MJLS) subject to noisy inputs and a quadratic performance index. Constraints appear on system state and input control variables in terms of the first two moments of the processes. The information available to the controller does not involve observations of the Markov chain state and, to solve the problem a sequence of linear feedback gains that is independent of the Markov state is adopted. The necessary conditions of optimality are provided by an equivalent deterministic form of expressing the stochastic MPC control problem subject to the constraints. A numerical solution that attains the necessary conditions for optimality and provides the feedback gain sequence is proposed. The solution is sought by an iterative method performing a variational search using a LMI formulation that takes the state and input constraints into account. © 2006 IEEE.
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Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-34047205721&partnerID=40&md5=f8bc4203c6935aa813bd5015a1dc2d63
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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