Abstract
A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of two-mass-resort-damper nonlinear oscillatory plant. The first MRTNN module identified the exponential part of the unknown plant and the second one - the oscillatory part of the plant. The plant has been controlled by a direct adaptive neural control system with integral term. The RTNN controller used the estimated parameters and states to suppress the plant oscillations and the static plant output control error is reduced by an I-term added to the control.
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References
Haykin, S.: Neural Networks, a Comprehensive Foundation, 2nd edn., sec. 2.13, 4.13, pp. 84–89, 208–213. Prentice-Hall, Upper Saddle River (1999)
Dreyfus, G.: Neural Networks: Methodology and Applications, 2nd edn. Springer, Heidelberg (2005)
Calderon, G., Draye, J.: Nonlinear Dynamic System Identification with Dynamic Recurrent Neural Networks. In: Proc. of the International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing, pp. 49–54 (1996) ISBN: 0-8186-7456-3
Song, J., Lee, K., Choi, J.: Vibration Control of Two Mass System Using a Neural Network Torsional Torque Estimator. In: Proc. of the 24th Annual Conference of the IEEE, IECON 1998, vol. 3, pp. 1785–1788 (1998) ISBN: 0-7803-4503
Yousefi, H., Hirvonen, M., Handroos, H., Soleymani, A.: Application of Neural Network in Suppressing Mechanical Vibration of a Permanent Magnet Linear Motor. Control Engineering Practice (16), 787–797 (2007), doi:10.1016/j.conengprac
Bouchard, M.: New Recursive Least-Squares Algorithms for Nonlinear Active Control of Sound and Vibration Using Neural Networks. IEEE Transactions on Neural Networks 12(1) (January 2001)
Luis, G., Teixeira, R., Ribero, J.: A Neural Network Based Direct Inverse Control for Active Control of Vibrations of Mechanical Systems. In: Proc. of the IEEE VI Brazilian Symposium on Neural Networks, pp. 107–112 (January 2000) ISBN: 0-7695-0856-1
Kazemy, A., Hosseini, S.A., Farrokhi, M.: Second Order Diagonal Recurrent Neural Network. In: IEEE International Symposium on Industrial Electronics, ISIE, pp. 251–256 (2007), doi:10.1109/isie.2007.4374607
Govindhasamy, J.J., McLoone, S.F., Irvin, G.W.: Second Order Training of Adaptive Critics for Online Process Control. IEEE Transactions on Systems, Man, and Cybernetics-part B: Cybernetics 35(2) (April 2005), doi:10.1109/TSMCB.2004.843276
Baruch, I.S., Garrido, R.: A Direct Adaptive Neural Control Scheme with Integral Terms. International Journal of Intelligent Systems, Special Issue on Soft Computing for Modelling, Simulation and Control of Nonlinear Dynamical Systems 20(2), 213–224 (2005) Castillo, O., Melin, P. (guest eds.) ISSN 0884-8173
Baruch, I.S., Mariaca-Gaspar, C.R.: A Levenberg-Marquardt Learning Applied for Recurrent Neural Identification and Control of a Wastewater Treatment Bioprocess. International Journal of Intelligent Systems 24, 1094–1114 (2009) ISSN 0884-8173
Sira-RamÃrez, H., Márquez, R., Rivas-Echeverria, F., Llanes-Santiago, O.: Control of Nonlinear Systems, 1st edn. Pearson Prentice Hall, Madrid (2005) (in Spanish) ISBN 84-205-4449-3
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Baruch, I., Hernandez, S.M., Moreno-Cruz, J., Gortcheva, E. (2012). Recurrent Neural Identification and an I-Term Direct Adaptive Control of Nonlinear Oscillatory Plant. In: Ramsay, A., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2012. Lecture Notes in Computer Science(), vol 7557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33185-5_24
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DOI: https://doi.org/10.1007/978-3-642-33185-5_24
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