Robust Kalman Filtering for Systems Subject to Polytopic Uncertainties

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: 2022 30TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED); v. N/A, p. 6-pg., 2022-01-01.

Resumo

Measuring the entire state vector of a dynamic system in practical applications is often infeasible due to various factors. State estimation is usually adopted to overcome this issue. However, the system model inevitably undergoes the degrading effects of parametric uncertainties. Hence, it is crucial to estimate the state irrespective of such unknown parameter variations. In this paper, we address the robust filtering problem regarding linear discrete-time systems subject to polytopic uncertainties. We formulate a min-max optimization problem whose cost function weights the polytope vertices altogether. The solution yields a robust recursive filter as a Kalman-like correction-prediction algorithm, suitable for real-time applications. We further provide stability conditions for the steady-state filter. Furthermore, we validate our approach with a numerical example, comparing the results with other methods from the literature on robust filtering. (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