Constrained model predictive control of jump linear systems with noise and non-observed Markov state

Autor(es)

Vargas, Alessandro N. ; Furloni, Walter ; do Val, Joao B. R. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2006 AMERICAN CONTROL CONFERENCE, VOLS 1-12; v. 1-12, p. 2-pg., 2006-01-01.

Resumo

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. (AU)

• Processo FAPESP: 04/06947-0 - Sistemas lineares sujeitos a saltos markovianos: estabilidade e controle com observacao incompleta da cadeia.
• Beneficiário: Alessandro Do Nascimento Vargas
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 03/06736-7 - Controle e filtragem de sistemas estocásticos markovianos com saltos nos parâmetros
• Beneficiário: João Bosco Ribeiro do Val
• Modalidade de apoio: Auxílio à Pesquisa - Temático