Abstract
In this paper, real time motion tracking of a robot manipulator based on the adaptive learning radial basis function network is proposed. This method for adaptive learning needs little knowledge of the plant in the design processes. So the centers and widths of the employed radial basis function network (RBFN) as well as the weights are determined adaptively. With the help of the RBFN, motion tracking of the robot manipulator is implemented without knowing the information of the system in advance. Furthermore, identification error and the tuned parameters of the RBFN are guaranteed to be uniformly ultimately bounded in the sense of Lyapunov’s stability criterion.
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References
Wai, R.J., Chen, P.C.: Intelligent Tracking Control for Robot Manipulator Including Actuator Dynamics via TSK-Type Fuzzy Neural Network. IEEE Trans. Fuzzy Syst. 12, 552–559 (2004)
Morris, A., Khemaissia, S.: Stable and fast neurocontroller for robot arm movement. IEE Proc.- Control Theory and Application 142, 378–384 (1996)
Gurkan, E., Erkmen, I., Erkmen, A.M.: Two-way fuzzy adaptive identification and control of a flexible-joint robot arm. Inf. Sci. 145, 13–43 (2003)
Liu, M.: Decentralized control of robot manipulators: nonlinear and adaptive approaches. IEEE Trans Automatic Control 44, 357–363 (1999)
Tayebi, A.: Adaptive iterative learning control for robot manipulators. Automatica 40, 1195–1203 (2004)
Kiguchi, K., Fukuda, T.: Robot Manipulator Contact Force Control Application of Fuzz-Neural Network. In: Proc. IEEE Intl. Conf. Robotics and Automation, pp. 875–880 (1995)
Nie, J., Linkens, D.A.: Learning control using fuzzified self-organizing radial basis function network. IEEE Trans Fuzzy Systems 1, 280–287 (1993)
Lin, C.J., Lin, C.T., Lee, C.S.G.: Fuzzy adaptive learning network with on-line neural learning. Fuzzy Sets and Systems 71, 25–45 (1995)
Sanner, R.M., Slotine, J.J.E.: Gaussian networks for direct adaptive control. IEEE Trans Neural Networks 3, 837–863 (1992)
Schaal, S., Atkeson, C., Vijayakumar, S.: Real-time robot learning with locally weighted statistical learning. In: Proc. IEEE Intl. Conf. Robotics and Automation, vol. 1, pp. 288–293 (2000)
Panchapakesan, C., Palaniswami, M., Ralph, D., Manzie, C.: Effects of moving the center’s in an RBF network. IEEE Trans. Neural Networks 13, 1299–1307 (2002)
Fu, X.J., Wang, L.P.: Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Trans. System, Man, Cybern, Part B Cybernetics 33, 399–409 (2003)
Bohte, S.M., La Poutre, H., Kok, J.N.: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans. Neural Networks 13, 426–435 (2002)
Rajapakse, J.C., Wang, L.P. (eds.): Neural Information Processing: Research and Development. Springer, Berlin (2004)
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Kim, D., Huh, SH., Seo, SJ., Park, GT. (2005). Use of Adaptive Learning Radial Basis Function Network in Real-Time Motion Tracking of a Robot Manipulator. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_139
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DOI: https://doi.org/10.1007/11539902_139
Publisher Name: Springer, Berlin, Heidelberg
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