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Title: Controle inteligente LQR neuro-genético para alocação de autoestrutura em sistemas dinâmicos multivariáveis
metadata.dc.creator: ABREU, Ivanildo Silva
metadata.dc.contributor.advisor1: FONSECA NETO, João Viana da
Keywords: Teoria do controle
Equação algébrica de Riccati
LQR (Regulador Linear Quadrático)
RNR (Rede Neural Recorrente)
Algoritmos genéticos
Sistemas de controle inteligente
Controle automático
Issue Date: 30-Aug-2008
Publisher: Universidade Federal do Pará
Citation: ABREU, Ivanildo Silva. Controle inteligente LQR neuro-genético para alocação de autoestrutura em sistemas dinâmicos multivariáveis. 2008. 247 f. Tese (Doutorado) - Universidade Federal do Pará, Instituto de Tecnologia, Belém, 2008. Programa de Pós-Graduação em Engenharia Elétrica.
Abstract: In this thesis is presented a neural-genetic model, oriented to state space controllers synthesis, based on the Linear Quadratic Regulator design, for eigenstructure assignment of multivariable dynamic systems. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network to perform the weighting matrices selection and the algebraic Riccati equation solution, respectively. In order to a assess the LQR design, the procedure was applied in a 6th order aircraft model, 6th order doubly fed induction generator model of a wind plant and a 4th order electric circuit model which were used to evaluate the fusion of the computational intelligence paradigms and the control design method performance.The performance of the neural-genetic models are evaluated by the first and second statistics moments for the genetic algorithm, whereas the neural network is evaluated by surfaces of the energy function and of the norm of the infinity of the algebraic equation of Riccati and the results compared to the results obtained by using Schur’s Method.
Appears in Collections:Teses em Engenharia Elétrica (Doutorado) - PPGEE/ITEC

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