Abstract
The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with friction. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed intelligent control system is confirmed by simulation and comparative experimental results, where a good convergent results, are obtained.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamic Systems using Neural Networks. IEEE Transactions on NNs 1, 4–27 (1990)
Sastry, P.S., Santharam, G., Unnikrishnan, K.P.: Memory Networks for Identification and Control of Dynamical Systems. IEEE Transactions on NNs 5, 306–320 (1994)
Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural Network for Control Systems-A Survey. Automatica 28, 1083–1112 (1992)
Baruch, I., Flores, J.M., Nava, F., Ramirez, I.R., Nenkova, B.: An Adavanced Neural Network Topology and Learning, Applied for Identification and Control of a D.C. Motor. In: Proc. of the First Int. IEEE Symposium on Intelligent Systems, Varna, Bulgaria, September 2002, pp. 289–295 (2002)
Baruch, I., Gortcheva, E.: Fuzzy Neural Model for Nonlinear Systems Identification. In: Proc. of the IFAC Worshop on Algorithms and Architectures for Real-Time Control, AARTC 1998, Cancun, Mexico, April 15-17, pp. 283–288 (1998)
Baruch, I., Garrido, R., Mitev, A., Nenkova, B.: A Neural Network Approach for Stick-Slip Model Identification. In: Proc. of the 5-th Int. Conf. on Engineering Applications of Neural Networks, EANN 1999, Warsaw, Poland, September 13-15, pp. 183–188 (1999)
Baruch, I., Flores, J.M., Martinez, J.C., Nenkova, B.: Fuzzy-Neural Models for Real-Time Identification and control of a Mechanical System. In: Cerri, S.A., Dochev, D. (eds.) AIMSA 2000. LNCS (LNAI), vol. 1904, pp. 292–300. Springer, Heidelberg (2000)
Mastorocostas, P.A., Theocharis, J.B.: A Recurrent Fuzzy-Neural Model for Dynamic System Identification. IEEE Trans. on SMC – Part B: Cybernetics 32, 176–190 (2002)
Lee, S.W., Kim, J.H.: Robust adaptive stick-slip friction compensation. IEEE Trans. on Ind. Electr. 42, 474–479 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baruch, I.S., Beltran L, R., Olivares, JL., Garrido, R. (2004). A Fuzzy-Neural Multi-model for Mechanical Systems Identification and Control. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-24694-7_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
eBook Packages: Springer Book Archive