Adaptive neuro-controller design based on MLP network
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Date
2010-10-16Author
Norhayati, Mohd Noor
A. S., Hashim
Mohd Yusoff, Mashor, Prof. Dr.
Siti Maryam, Sharun
Azian Azamimi, Abdullah
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Show full item recordAbstract
Back Propagation (BP) algorithm is the most
commonly used algorithm for training artificial neural
networks. But, it suffers from extensive computations,
relatively slow convergence speed and possible
divergence for certain conditions. The main objective
of this paper was to compare the performance of BP
algorithm and Recursive Least Square (RLS) algorithm
for Adaptive Neuro-Controller (ANC). These
algorithms are used to update the parameter of the
ANC. A neural network model, called Multi Layered
Perceptron (MLP) network is used for this ANC. The
Model Reference Adaptive Control (MRAC) is used to
generate the desired output path and to ensure the
output of the controlled system follows the output of
reference model. In this paper, the comparison
between two algorithms is based on the convergence
speed and robustness of the controller. These
controllers have been tested using a linear and a
nonlinear plant with several varying operating
conditions. The simulation results show that RLS
algorithm have better performance compared to BP
algorithm.
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