GA-based neural fuzzy control of flexible-link manipulators
Journal article
Siddique, MNH and Tokhi, MO (2006). GA-based neural fuzzy control of flexible-link manipulators. Engineering Letters. 13 (2), pp. 148-157.
Authors | Siddique, MNH and Tokhi, MO |
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Abstract | The limitations of conventional model-based control mechanisms for flexible manipulator systems have stimulated the development of intelligent control mechanisms incorporating fuzzy logic and neural networks. Problems have been encountered in applying the traditional PD-, PI-, and PID-type fuzzy controllers to flexible-link manipulators. A PD-PI-type fuzzy controller has been developed where the membership functions are adjusted by tuning the scaling factors using a neural network. Such a network needs a sufficient number of neurons in the hidden layer to approximate the nonlinearity of the system. A simple realisable network is desirable and hence a single neuron network with a nonlinear activation function is used. It has been demonstrated that the sigmoidal function and its shape can represent the nonlinearity of the system. A genetic algorithm is used to learn the weights, biases and shape of the sigmoidal function of the neural network. |
Keywords | Fuzzy control; Flexible-link manipulators; Genetic algorithms; Neuro-fuzzy control |
Year | 2006 |
Journal | Engineering Letters |
Journal citation | 13 (2), pp. 148-157 |
Publisher | International Association of Engineers |
ISSN | 1816-093X |
Publication dates | |
Online | 04 Aug 2006 |
Publication process dates | |
Accepted | 15 Jan 2006 |
Deposited | 29 Apr 2020 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/89q00
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Accepted author manuscript
Siddique&Tokhi_EL-2006_13(2)_148-157.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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