Abstract
The aim of this paper is to investigate a control method for robotic agent, operating in ubiquitous network. It is difficult for people to fulfill their tasks under dynamically changing environment. Therefore, robotic agent performs tasks in stead of people. The ubiquitous network makes it possible to connect between robotic agent and people. Because robotic agent should fulfill tasks according to the received commands from people, the adaptive control method for the agent is needed in order to do the given tasks properly. This paper introduces an adaptive tracking control method for robotic agent based on the radial based functions network (RBFN). When some commands are received through networks, the proposed method can make robotic agent possible to perform tasks under dynamically changing environment. Experimental results show that the proposed control method based on RBFN is adaptable to the environment changes and is more robust than the conventional PID control method and the neuro-control method based on the multilayer perceptron.
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References
Fu, R.K.S., Gonzalez, R.C., Lee, C.S.G.: Robotics’. McGraw-Hill, New York (1987)
Spong, M.W., Vidyasagar, M.: Robot Dynamic and Control. John Wiley & Sons, Chichester (1989)
Slotine, J.-J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. On Neural Networks 1(1), 4–27 (1990)
Abido, M.A., Abdel-Magid, Y.: On-line identification of synchronous machines using radial basis function neural networks. IEEE Trans. on Power Systems 12(4), 1500–1506 (1997)
Morris, A.S., Khemaissia, S.: A neural network based adaptive robot controller. Journal of Intelligent and Robotic Systems 15, 3–10 (1996)
Meddah, D.Y., Benallegue, A.: A stable neuro-adaptive controller for rigid robot manipulators. Journal of Intelligent and Robotic Systems 20, 181–193 (1997)
Carelli, R., Camacho, E.F.: A neural network based feedforward adaptive controller for robots. IEEE Trans. On Systems, Man and Cybernetics 25(9), 1281–1288 (1995)
Zhihong, M., Wu, H.R., Palaniswame, M.: An adaptive tracking controller using neural networks for a class of nonlinear systems. IEEE Trans. on Neural Networks 9(5), 947–955 (1998)
Lewis, F.L., Liu, K., Yesildirek, A.: Neural-net robot controller with guaranteed tracking performance. IEEE Trans. on Neural Networks 6(3), 703–715 (1995)
Seshagiri, S., Khalil, H.K.: Output feedback control of nonlinear systems using rbf neural networks. IEEE Trans. on Neural Networks 11(1), 69–79 (2000)
Sanner, R.M., Slotine, J.-J.E.: Gaussian networks for direct adaptive control. IEEE Trans. on Neural Networks 3(6), 837–863 (1992)
Jagannathan, S., Lewis, F.L., Pastravanu, O.: Model reference adaptive control nonlinear dynamical systems using multilayer neural networks. In: Proc. of IEEE Int. Conf. on Neural Networks, vol. 7, pp. 4766–4771 (1994)
Patirio, H.D., Liu, D.: Neural network-based model reference adaptive control system. IEEE Trans. on Systems, Man and Cybernetics 30(1), 198–204 (2000)
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© 2007 Springer-Verlag Berlin Heidelberg
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Lee, MJ., Hwang, GH., Jang, WT., Cha, KH. (2007). Robotic Agent Control Based on Adaptive Intelligent Algorithm in Ubiquitous Networks. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2007. Lecture Notes in Computer Science(), vol 4496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72830-6_56
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DOI: https://doi.org/10.1007/978-3-540-72830-6_56
Publisher Name: Springer, Berlin, Heidelberg
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