Abstract
A six-degree of freedom (dof) robotic manipulator from Stanford family is controlled with an intelligent control system designed by using Elman network and generalized predictive control (GPC) algorithm. Three of Elman networks are trained by using GPC based data. They are used in parallel form to improve the reliability of the system by error minimization. At the end of parallel implementation, the results of networks are evaluated by using torque equations to select the network with best result. Simulation based test results showed that the proposed controller improves the performance of the system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kasparian, V., Batur, C.: Model Reference Based Neural Network Adaptive Controller. ISA Transactions 37, 21–39 (1998)
Huang, D., Cauwenberghe, V.A.R.: Neural Network Based Multiple Feedback Long Range Predictive Control. Neurocomputing 18, 127–139 (1998)
Donat, S., Bhat, N., Mc Avoy, T.J.: Neural Net Based Model Predictive Control. Int. J. of Control 54(6), 1453–1468 (1991)
Rensen, P.H.S., Rgaard, M.N., Ravn, O., Poulsen, N.K.: Implementation of Neural Network Based Nonlinear Predictive Control. Neurocomputing 28, 37–51 (1999)
Gupta, P., Sinha, N.K.: Intelligent Control of Robotic Manipulators: Experimental Study Using Neural Networks. Mechatronics 10, 289–305 (2000)
Koker, R.: Model Based Intelligent Control of 3-Joint Robotic Manipulator with Machine Vision System. Phd Thesis, Science Institute of Sakarya University (2002)
Koker, R., Ferikoglu, A.: Model Based Intelligent Control of a 3-Joint Robotic manipulator: A Simulation Study Using Artificial Neural Networks. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.) ISCIS 2004. LNCS, vol. 3280, pp. 31–40. Springer, Heidelberg (2004)
Koker, R., Oz, C., Kazan, R.: Vision Based Robot Control Using Generalized Predictive Control. In: Intern. Conf. on Electrics and Electronics Eng. ELECO 2001, Bursa (2001)
Ozsoy, C., Kazan, R.: Self-tuning Predictive Controllers For Robotic Manipulators. In: 2. Project Workshop on Cim and Robotics Applications, Regional UNDP/UNIDO Project, Belgrad, pp. 99–115 (1991)
Koker, R., Ekiz, H., Boz, A.F.: Design and Implementation of a Vision Based Control System Towards Moving Object Capture For a 3-Joint Robot. In: 11th Mediterranean Conf. on Control and Automation, Rhodes, Greece, p. 55 (2003)
Borinson, U.: Self-tuning Regulators for a Class of Multivariable Systems. Automatica 15, 209–215 (1979)
Acosta, L., Marichal, G.N., Moreno, L., Rodrigo, J.J., Hamilton, A., Mendez, J.A.: A Robotic System Based On Neural Network Controllers. Artificial Intelligence in Engineering 13, 393–398 (1999)
Fu, K.S., Gonzalez, R.C., Lee, C.S.G.: Robotics: Control, Sensing, Vision and Intelligence, p. 38. Mcgraw-Hill Book Company, New York (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Akbas, A. (2005). Intelligent Predictive Control of a 6-Dof Robotic Manipulator with Reliability Based Performance Improvement. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_36
Download citation
DOI: https://doi.org/10.1007/11508069_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
eBook Packages: Computer ScienceComputer Science (R0)