Propostas de metodologias para identificação e controle inteligentes

AUTOR(ES)
DATA DE PUBLICAÇÃO

2005

RESUMO

This thesis presents proposals of methodologies for intelligent identification and control. A methodology tor nonlinear dynamic discrete time systems identification, based on the instrumental variable method and Takagi-Sugeno fuzzy model, is presented. In this methodology, which is an extension of the standard instrumental variable method, the chosen instrumental variables, estatistically independent of the noise, are mapped into fuzzy sets, partitioning the input space in subregions, for unbiased estimation of Takagi-Sugeno fuzzy model consequent parameters in a noisy environment. A gain scheduling adaptive control design based on neural network, fuzzy systems and genetic algorithms for nonlinear dynamic discrete time systems is also presented. The fuzzy controller is developed and designed by a genetic algorithm to satisfy, simultaneously, multiple objectives. "With the supervised learning scheme, the fuzzy controller parameters are used to design the gain neural scheduler to tune on-line the fuzzy controller in some operation points of the dynamic system

ASSUNTO(S)

sistemas inteligentes de controle systems identification genetic algorithms variaveis instrumentais (estatistica) intelligent control systems identificação de sistemas algoritmos geneticos neural networks (computer) redes neurais (computação) fuzzy systems instrumental variables (statistics) sistemas nebulosos

ACESSO AO ARTIGO

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