Desacoplamento de um gerador síncrono através de um controle adaptativo por modelo de referência baseado em funções de Base radial
An alternative nonlinear technique for decoupling and control is presented. This technique is based on a RBF (Radial Basis Functions) neural network and it is applied to the synchronous generator model. The synchronous generator is a coupled system, in other words, a change at one input variable of...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/15386 |
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| Resumo: | An alternative nonlinear technique for decoupling and control is presented. This technique
is based on a RBF (Radial Basis Functions) neural network and it is applied to the
synchronous generator model. The synchronous generator is a coupled system, in other
words, a change at one input variable of the system, changes more than one output. The
RBF network will perform the decoupling, separating the control of the following outputs
variables: the load angle and flux linkage in the field winding. This technique does not
require knowledge of the system parameters and, due the nature of radial basis functions,
it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied
in control. The RBF decoupler is designed in this work for decouple a nonlinear
MIMO system with two inputs and two outputs. The weights between hidden and output
layer are modified online, using an adaptive law in real time. The adaptive law is developed
by Lyapunov s Method. A decoupling adaptive controller uses the errors between
system outputs and model outputs, and filtered outputs of the system to produce control
signals. The RBF network forces each outputs of generator to behave like reference
model. When the RBF approaches adequately control signals, the system decoupling is
achieved. A mathematical proof and analysis are showed. Simulations are presented to
show the performance and robustness of the RBF network |
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