Controle de sistemas não lineares atraves de redes neurais

AUTOR(ES)

Edilberto Pereira Teixeira

DATA DE PUBLICAÇÃO

1991

RESUMO

This thesis presents an approach for controlling a nonlinear system using an adaptive scheme implemented through neural networks. For a class of systems. necessary conditions for teaching the system input-output mapping to a multi-layer neural network trained by the generalized deIta rule are presented. A feedback linearization procedure, using feedforward neural networks, is also introduced in this work. The method can be applied for unknown continuous time systems as well as discrete time or sampled systems. The feedback linearization procedure can be either adaptively accomplished, having the neural network weights adjusted during normal operation of the plant, or in a non adaptive mode, having the weights adjusted off-line. In the proposed scheme, two neural networks are trained in two different stages. In the first stage, the nonlinear system is exclted several times to teach the inverse dynamics of the system to a neural network. In the second stage. the system is again excited several times to train a second neural network with input signals that desired way

ASSUNTO(S)

industrias - automação engenharia de sistemas

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