Robust Regulation of Markov Jump Linear Systems with Uncertain Polytopic Transition Probabilities

Autor(es)

Bueno, Jose Nuno A. D. ; Marcos, Lucas B. ; Rocha, Kaio D. T. ; Terra, Marco H. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2022 EUROPEAN CONTROL CONFERENCE (ECC); v. N/A, p. 6-pg., 2022-01-01.

Resumo

In this paper, we examine the robust regulation problem of Markov jump linear systems with transition probabilities matrix subject to polytopic uncertainties. Based on penalty function and dynamic programming concepts, we define an unconstrained least-squares optimization problem from a polytopic perspective. In this sense, we minimize the states, whereas the underlying Markov jump system undergoes the worst-case effects of uncertainties. We propose a recursive solution in the form of a symmetrical matrix arrangement, from which we attain robust feedback gains. Moreover, the solution exists for any positive penalty parameter, whose a priori tuning is thus straightforward. For validation purposes, we provide a numerical example and compare the results to those obtained with a robust H-infinity controller. The proposed approach outperforms the robust H-infinity controller in terms of computational time required to yield the feedback gains. (AU)

• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 21/08103-0 - Controle de sistemas veiculares heterogêneos autônomos e cooperativos
• Beneficiário: Lucas Barbosa Marcos
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 17/16346-4 - Controle tolerante a falhas de rede de comunicação para o movimento coordenado de robôs heterogêneos
• Beneficiário: Kaio Douglas Teófilo Rocha
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto