Proposta de um filtro de partículas aliado ao filtro de Kalman estendido iterativo para estimação de estados de sistemas não lineares com ruído Gaussiano

• Ano de defesa: 2018
• Autor(a) principal: PROHMANN, Eric Antony Vinhaes
• Orientador(a): SOUZA, Francisco das Chagas de
• Banca de defesa: SERRA, Ginalber Luiz de Oliveira , RIBEIRO, Áurea Celeste da Costa
• Tipo de documento: Dissertação
• 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: Estimação de estados; Filtro de Kalman; Filtro de partículas
• Palavras-chave em Inglês: State estimation; Kalman filter; Particle filter.
• Área do conhecimento CNPq: Engenharia Elétrica
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2234

About the century of 1900, control systems techniques using states feedback began to get on the highlights. Such techniques need the state vector to be avaliable, what is not always possible to do with measurement equipments. So, techniques which implement state estimation became the center of attention of researchers. These state estimators use the system dynamic information and the input and output signals to estimate the states. The state estimator known as Kalman filter is the most acceptable and useful solution to linear systems and it is acknowledged as the solution to linear systems state estimation problem. Nonlinear systems, however, have no generic estimation method defined. The most famous nonlinear technique has been the extended Kalman filter, which is the first choice of application to many systems. On 1990s, another technique called particle filter got the spotlights, because the technological improvement allowed its implementation. The particle filter has been a technique which has shown good results on nonlinear systems state estimation. In this dissertation, it is proposed a particle filter with sampling importance resampling allied to iterated extended Kalman filter (FPA-FKEI) to nonlinear systems state estimation. The efficiency of the proposed method is proven through Monte Carlo realizations in 3 systems, a monovariable, a inverted-pendulum car and an electrical power system with 4 generators.