GA-based neural fuzzy control of flexible-link manipulators
Siddique, MNH and Tokhi, MO (2006). GA-based neural fuzzy control of flexible-link manipulators. Engineering Letters. 13 (2), pp. 148-157.
|Siddique, MNH and Tokhi, MO
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.
|Fuzzy control; Flexible-link manipulators; Genetic algorithms; Neuro-fuzzy control
|13 (2), pp. 148-157
|International Association of Engineers
|04 Aug 2006
|Publication process dates
|15 Jan 2006
|29 Apr 2020
|Accepted author manuscript
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