Abstract
Neural identification techniques are very useful for the problem of unknown dynamics and uncertainties during the development of a model that accurately represents the behaviour of a robot. In this paper we use the model of a Recurrent Trainable Neural Network (RTNN) for modelling a cylindrical robot. The RTNN proposal is a multilayer network local feedback into the single hidden layer, to approach the robot dynamics. The learning algorithm for this topology is the Backpropagation (BP) dynamic. The simulation results of the approximation obtained through RTNN showed a good convergence and accurate tracking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baruch IS, Flores JM, Nava F, Ramirez IR, Nenkova B (2002) An advanced neural network topology and learning, applied for identification and control of a D.C. motor. In: Proceedings of 1st international IEEE symposium on intelligent systems, pp 289−295
Baruch IS, Barrera-Cortes J, Hernandez LA (2004) A fed-batch fermentation process identification and direct adaptive neural control with integral term. In: Monroy R, Arroyo-Figueroa G, Sucar LE, Sossa H (eds) MICAI 2004: advances in artificial intelligence, LNAI, vol 2972. Springer, Heidelberg, pp 764−773
Baruch IS, Georgieva P, Barrera-Cortes J, Feyo de Azevedo S (2005) Adaptive recurrent neural network control of biological wastewater treatment. Int J Intell Syst 20:173−194 (Wiley Periodicals, Inc. N. J.)
Ge SS, Lee TH, Harris CJ (1998) Adaptive neural network control of robotic manipulators. World Scientific Publishing Co., London
Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall, Upper Saddle River
Horne B, Jamshidi M, Vadiee N (1990) Neural networks in robotics: a survey. J Intell Robot Syst 3:51−66
Khosla P, Kanade T (1987) An algorithm to estimate manipulator dynamics parameters. Institute for Software Research, Irvine Paper 653
Narendra KS, Parthasarathy K (1990) Identification and control of dynamic systems using neural networks. IEEE Trans Neural Networks 1:4−27
Patino HD, Carelli R, Kuchen BR (2002) Neural networks for advanced control of robot manipulators. IEEE Trans Neural Networks 13:343−354
Spong MW, Vidyasagar M (1989) Robot dynamics and control. Wiley, New York
Wan E, Beaufays F (1996) Diagrammatic method for deriving and relating temporal neural networks algorithms. Neural Comput 8:182−201
Acknowledgments
The authors acknowledge the support provided to the Secretaría de Investigación y Posgrado of IPN (SIP-IPN), Comisión de Operación y Fomento de Actividades Académicas of IPN (COFAA-IPN), Consejo Nacional de Ciencia y Tecnología (CONACyT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mariaca Gaspar, C.R., Velázquez-Velázquez, J.E., Tovar Rodríguez, J.C. (2015). Identification of a Cylindrical Robot Using Recurrent Neural Networks. In: Ceccarelli, M., Hernández Martinez, E. (eds) Multibody Mechatronic Systems. Mechanisms and Machine Science, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-09858-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-09858-6_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09857-9
Online ISBN: 978-3-319-09858-6
eBook Packages: EngineeringEngineering (R0)