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Optimal Sampled-Data Control of Markov Jump Linear Systems through Differential LMIs

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Autor(es):
Gabriel, Gabriela W. ; Geromel, Jose C. ; Goncalves, Tiago R. ; IEEE
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC); v. N/A, p. 6-pg., 2017-01-01.
Resumo

This paper addresses the problem of designing a sampled-data state feedback control law for continuous-time Markov jump linear systems (MJLS). The main goal is to characterize the optimal solution of this class of problems in the context of H-2 and H-infinity performances. The theoretical achievements are based on the direct application of the celebrated Bellman's Principle of Optimality expressed in terms of the dynamic programming equation associated to the time interval corresponding to two successive sampling instants. The design conditions are expressed through Differential Linear Matrix Inequalities (DLMI). The proposed method is simpler than those available in the literature to deal with this kind of systems since it is implemented without the necessity of an iterative algorithm. An example is solved for illustration. (AU)

Processo FAPESP: 16/06343-5 - Teoria Unificada para o Controle Amostrado de Sistemas Dinâmicos Híbridos
Beneficiário:Gabriela Werner Gabriel
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 16/08043-9 - Desigualdades diferenciais lineares: solução numérica e aplicações
Beneficiário:Tiago Rocha Gonçalves
Modalidade de apoio: Bolsas no Brasil - Mestrado