Abstract
Recently, there has been a great deal of interest in intelligent control of robotic manipulators. Artificial neural network (ANN) is a widely used intelligent technique on this way. Using ANN, these controllers learn about the systems to be online controlled by them. In this paper, a neural network controller was designed using traditional generalized predictive control algorithm (GPC). The GPC algorithm, which belongs to a class of digital control methods and known as Model Based Predictive Control, require long computational time and can result in a poor control performance in robot control. Therefore, to reduce the process time, in other words, to avoid from the highly mathematical computational structure of GPC, a neural network was designed for a 3-Joint robot. The performance of the designed control system was shown to be successful using the simulation software, which includes the dynamics and kinematics of the robot model.
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References
Gupta, P., Sinha, N.K.: Intelligent control of robotic manipulators: Experimental study using neural networks. Mechatronics 10, 289–305 (2000)
Jang, J.O.: Implementation of indirect neuro-control for a nonlinear two robot MIMO system. Control Engineering Practice 9, 89–95 (2001)
Kasparian, V., Batur, C.: Model reference based neural network adaptive controller. ISA Transactions 37, 21–39 (1998)
Willis, M., Montague, G., Massimo, C., Tham, M., Morris, A.: Artificial neural networks in process estimation and control. Automatica 28(6), 1181–1187 (1992)
Soloway, D., Haley, P.: Neural/generalized predictive control, a Newton-Raphson implementation. In: Proceeding of The Eleventh IEEE International Symposium on Intelligent Control, pp. 277–282 (1996)
Arahal, M.R., Berenguel, M., Camacho, E.F.: Neural identification applied to predictive control of a solar plant. Control Engineering Practice 6, 333–344 (1998)
Clarke, D.W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control, part-1: The basic algorithm. Automatica 23(2), 137–148 (1987)
Koker, R., Oz, C., Kazan, R.: Vision based robot control using generalized predictive control. In: Intern. Conf. on Electrics and Electronics Engineering, Bursa, Turkey, pp. 236–240 (2001)
Omatu, S., Khalid, M., Yusof, R.: Neuro-control and its applications, 2nd edn. Springer, Heidelberg (1996)
Koker, R.: Model Based intelligent control of 3-joint robotic manipulator with machine vision system. PhD. Thesis, Sakarya University, Science Institute (2002)
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)
Kao, C.K., Sinha, A., Mahalanabis, A.K.: A digital algorithm for near-minimum-time control of robotic manipulators. Journal of Dynamic Sys., Measurement and Control 109, 320–327 (1987)
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 (2003)
Köker, R., Oz, C., Ferikoglu, A.: Development of a vision based object classification system for a robotic manipulator. In: 8th IEEE International Conference on Electronics, Circuits and Systems, Malta, pp. 1281–1284 (2001)
Haykin, S.: Neural networks. Macmillan College Publishing Company, Basingstoke (1994)
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© 2004 Springer-Verlag Berlin Heidelberg
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Koker, R., Ferikoglu, A. (2004). 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) Computer and Information Sciences - ISCIS 2004. ISCIS 2004. Lecture Notes in Computer Science, vol 3280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30182-0_4
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DOI: https://doi.org/10.1007/978-3-540-30182-0_4
Publisher Name: Springer, Berlin, Heidelberg
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