Estimating and control of Markov jump linear systems with partial observation of the operation mode.

• Ano de defesa: 2018
• Autor(a) principal: Oliveira, André Marcorin de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Cadeias de Markov; Controle ótimo; Controle robusto; Linear systems; Markov chains; Optimal control; Otimização convexa; Robust control Convex optimization.; Sistemas lineares
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3139/tde-01032019-144518/

In this thesis, we present some contributions to the Markov jump linear systems theory in a context of partial information on the Markov chain. We consider that the state of the Markov chain cannot be measured, but instead there is only an observed variable that could model an asynchronous phenomenon between the application and the plant, or a simple fault detection and isolation device. In this formulation, we investigate the problem of designing controllers and filters depending only on the observed variable in the context of H2, H?, and mixed H2/H? control theory. Numerical examples and academic applications are presented for active-fault tolerant control systems and networked control systems.