Nonlinear Generalized Predictive Controller Based on Artificial Neural Network for Robot Control


	Journal of Applied Sciences
	Year: 2008 \| Volume: 8 \| Issue: 21 \| Page No.: 3783-3794 DOI: 10.3923/jas.2008.3783.3794

Nonlinear Generalized Predictive Controller Based on Artificial Neural Network for Robot Control

B. Durmus and N. Yumusak

Abstract: This study deals with the tracking control problem of a robotic manipulator with changing dynamics. A multiple-input multiple-output (MIMO) artificial neural network based generalized predictive control (NGPC) controller was designed for a six-degrees-of-freedom (6-DOF) robotic manipulator random disturbances and changing load. A three-layered neural network was used in the controller design to predict robotic manipulator inputs which track a desired trajectory. Standard back propagation (BP) algorithm was used as a learning algorithm to minimize the difference between actual trajectory and that predicted by the neural network. NGPC controller was compared the conventional GPC under different control conditions. Results show that proposed control improved the ability of GPC under uncertainties.

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How to cite this article:

B. Durmus and N. Yumusak, 2008. Nonlinear Generalized Predictive Controller Based on Artificial Neural Network for Robot Control. Journal of Applied Sciences, 8: 3783-3794.

DOI: 10.3923/jas.2008.3783.3794

URL: https://scialert.net/abstract/?doi=jas.2008.3783.3794

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