Abstract
The paper describes a unified algorithm for both parametric and structural identification. The approach combines three typical techniques such as neural networks, statistics and genetic algorithm. A specific structure of the neural network is used that allows to design a controller directly from parameters of the identified model. The control strategy based on reference model is discussed. Finally, the proposed solution is illustrated by numerical example.
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References
Anderson, S.R., Kadirkamanathan, V.: Modelling and identification of nonlinear deterministic systems in delta-domain. Automatica 43, 1859–1868 (2007)
Berana, J., Ghoshb, S., Schella, D.: On least squares estimation for long-memory lattice processes. Journal of Multivariate Analysis 100(10), 2178–2194 (2009)
Billings, S.A., Fadzil, M.B.: The practical identification of systems with nonlinearities. In: The 7th IFAC/IFORS Symposium on Identification and System Parameter Estimation, York, UK, pp. 155–160 (July 1985)
Caravani, P., Watson, M.L.: Recursive least-square time domain identification of structural parameters. Journal of Applied Mechanics 44(1), 135–140 (1977)
Gao, H., Meng, X., Chen, T.: A parameter-dependent approach to robust H ∞ filtering for time-delay systems. IEEE Transactions on automatic control 53(10), 2420–2425 (2008)
Hu, X.J., Lagakos, S.W., Lockhart, R.A.: Generalized least squares estimation of the mean function of a counting process based on panel counts. Statistica Sinica 19, 561–580 (2009)
Kotta, Ü., Zinober, A.S.I., Liu, P.: Transfer equivalence and realization of nonlinear higher order input-output difference equations. Automatica 37(11), 1771–1778 (2001)
Leontaritis, I.J., Billings, S.A.: Input-output parametric models for non-linear systems Part I: deterministic non-linear systems. International Journal of Control 41(2), 303–328 (1985)
Leva, A., Piroddi, L.: A neural network-based technique for structural identification of SISO systems 1, 135–138 (1994)
Nõmm, S., Kotta, Ü.: Comparison of neural networks-based ANARX and NARX models by application of correlation tests. In: International Joint Conference on Neural Networks, San Jose, CA, USA, pp. 2113–2118 (July-August 2011)
Petlenkov, E.: NN-ANARX structure based dynamic output feedback linearization for control of nonlinear MIMO systems. In: The 15th Mediterranean Conference on Control and Automation, Athena, Greece, pp. 1–6 (June 2007)
Petlenkov, E.: Model reference control of nonlinear systems by dynamic output feedback linearization of neural network based ANARX models. In: The 10th International Conference on Control Automation Robotics and Vision, Hanoi, Vietnam, pp. 1119–1123 (December 2008)
Pothin, R., Kotta, Ü., Moog, C.H.: Output feedback linearization of nonlinear discrete time systems. In: The IFAC Conference on Control Systems Design, Bratislava, Slovak Republic, pp. 181–186 (2000)
Shinozuka, M., Yun, C.B., Imai, H.: Identification of linear structural dynamic systems. Journal of the Engineering Mechanics Division 108(6), 1371–1390 (1982)
Sivanandam, S.N., Deepa, S.: Introduction to Genetic Algorithms. Springer, Berlin (2008)
Vassiljeva, K., Petlenkov, E., Nomm, S.: Evolutionary design of the closed loop control on the basis of NN-ANARX model using genetic algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part I. LNCS, vol. 7663, pp. 592–599. Springer, Heidelberg (2012)
Whitley, D.: Genetic algorithms and neural networks. In: Genetic Algorithms in Engineering and Computer Science, pp. 191–201. John Wiley & Sons Ltd. (1995)
Zhang, L.F., Zhu, Q.M., Longden, A.: A correlation-test-based validation procedure for identified neural networks. IEEE Transactions on Neural Networks 20(1), 1–13 (2009)
Zhu, Q.M., Zhang, L.F., Longden, A.: Development of omni-directional correlation functions for nonlinear model validation. Automatica 43, 1519–1531 (2007)
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Belikov, J., Petlenkov, E., Vassiljeva, K., Nõmm, S. (2013). Computational Intelligence Methods Based Design of Closed-Loop System. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_28
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DOI: https://doi.org/10.1007/978-3-642-42054-2_28
Publisher Name: Springer, Berlin, Heidelberg
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