MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
Main Article Content
Abstract
In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet function. This approach has been performed very successfully, with better results
obtained with the FFNN with modified wavelet activation function (FFMW) when compared with classic
FFNN with Sigmoid activation function (FFS) .One can notice from the simulation that the FFMW can be
capable of identifying the 4-Links of SCARA robot more efficiently than the classic FFS.
Article Details
How to Cite
Publication Dates
References
and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks”, IEEE International Symposium on
Intelligent Control Part of IEEE Multiconference on Systems and Control San Antonio, Texas, USA, September 3-5,2008.
Akamatsu S. Konishi M. and Imai J.,” Position Control of 2-Link SCARA Robot by using Internal Model Control”,
Memoirs of the Faculty of Engineering, Okayama University, Vol. 43, pp. 49-54, January 2009
Chaturvedi D., “Soft Computing Techniques and its Applications in Electrical Engineering”, Springer-Verlag Berlin Heidelberg, 2008
Er-Wei B., Tempo R. and Liu Y.” Identification of IIR Nonlinear Systems Without Prior Structural Information”,IEEE Transection on Automatic Control, Vol. 52, No. 3, March ,2007.
Hui J. Z.,”Dynamic Parameter Identification Of Robots Using A Neural Network as A Compensator”, The ANNALS of "DUNAREA De Jos" University of Galati Fascicle Xivmechanical Engineering, ISSN 1224-5615 2009
Jiang Z., Ishida T. and Sunawada M.” Neural Network Aided Dynamic Parameter Identification of Robot Manipulators”, IEEE International Conference on Systems, Man, and Cybernetics October 8-11, 2006, Taipei, Taiwan
Khireddine M. S. and Boutarfa A. ” Reconfigurable Control For A Scara Robot Using RBF Networks”. Journal of Electrical Engineering, Vol. 61, No. 2, 2010, pp. 100–106.
Kuraz, Y.,” Identification and Control of Robot Manipulator Using Adaptive Fuzzy Wavelet Network”, Ph.D. thesis, University of Baghdad, 2005.
Mosquera V., and Vivas A., “Robust Control For A SCARA Robot With Parametric Uncertainty”, University of Cauca, Popayán, Colombia Ustarth,2006.
Passold F., “Applying RBF Neural Nets for Position Control of an Inter/Scara Robot”, Int. J. of Computers, Communications & Control, ISSN 1841- 9836, E-ISSN 1841-9844 Vol. IV (2009), No. 2, pp. 148-157.
Spong M. W., Hutchinson S., and Vidyasagar M.,” Robot Dynamics and Control”, Second Edition, January 28, 2004.