Filtragem de Kalman Fuzzy Tipo-2 Intervalar Evolutiva Baseada em Processamento Espectral de Dados Experimentais

• Ano de defesa: 2024
• Autor(a) principal: GOMES, Daiana Caroline dos Santos
• Orientador(a): SERRA, Ginalber Luiz de Oliveira
• Banca de defesa: SERRA, Ginalber Luiz de Oliveira , SOUZA, Francisco das Chagas de , SANTANA, Ewaldo Eder Carvalho , ATTUX, Romis Ribeiro de Faissol , MUNARO, Celso José
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Identificação de Sistemas; Filtragem de Kalman; Modelo fuzzy tipo-2 intervalar; Análise Espectral Singular; Sistemas fuzzy evolutivos
• Palavras-chave em Inglês: Systems identification; Kalman filtering; Interval type-2 fuzzy model; Singular spectral analysis; Evolving fuzzy systems
• Área do conhecimento CNPq: Modelos Analíticos e de Simulação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/6351

In this doctoral thesis, an interval type-2 evolving fuzzy Kalman filter is proposed for interval processing of uncertain experimental data from unobservable spectral components. The adopted methodology considers the following steps: an initial model of the interval type2 evolving fuzzy Kalman filter is identified offline from an initial window of experimental data; the updating of the antecedent proposition of interval type-2 evolving fuzzy Kalman filter is performed using a interval type-2 fuzzy formulation of the evolving Takagi-Sugeno (eTS) clustering algorithm and the consequent proposition update is performed using a interval type-2 fuzzy formulation of the Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable spectral components extracted from the experimental data by means of an algorithm of Multivariate Recursive Singular Spectral Analysis. The computational results for interval tracking of the Mackey-Glass chaotic time series and for forecasting of the trajectory described by aerospace vehicles, in a noisy environment, illustrate the efficiency of the proposed methodology compared to relevant approaches in the literature. The experimental results regarding the interval tracking of the dynamic behavior of COVID-19 and for filtering and tracking a 2DoF helicopter are presented, in order to elucidate the applicability of the proposed methodology.