RBF Neural Network Based Adaptive Tracking Control for a Class of Nonlinear Plant Using Stochastic U-Model

Abstract:

In this paper an adaptive tracking control algorithm and its step by step implementation procedure are developed for a class of nonlinear plants within a U-model framework with unknown parameters. With the author’s previous justification, not only the control oriented model represents a wide range of smooth (polynomial) nonlinear dynamic plants (without using linearisation approximation at all), but also make almost all linear control system design techniques directly applicable (with a root solver bridging the linear design and calculation of controller output). A new technique is proposed to design an online control algorithm using the Radial Basis Functions Neural Network (RBFNN). The plant parameters are estimated online and are used to update the weights of the RBFNN. The weights update equations are derived based on the well known LMS (least mean square). A number of simulated case studies are conducted to illustrate the efficiency of the claimed insight and design procedure.

• Periodical: Key Engineering Materials (Volumes 474-476)
• Pages: 1209-1214
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.474-476.1209
• Online since: April 2011
• Authors: Bin Wang, Cai Liu, Xue Li Wu, Lei Liu
• Keywords: Discrete Time System, On-Line Identification, RBFNN, Time Varying Parameter, U-Mode

References

[1] Ding, F. and Chen, T. W: Identification of Hammerstein nonlinear ARMAX systems. Automatic, 2005, 41(9), 1479-1489.

DOI: 10.1016/j.automatica.2005.03.026

[2] Zhu, Q. M. and Guo, L. Z.: A pole placement controller for non-linear dynamic plants. IMechE, J. Systems and Control Engineering, 2002, 216(16), 467-476.

[3] Xu, X. Y.: Adaptive control theory and applications, 2007 (publishing house of electronics industry).

[4] Yan, Q. J. : Numerical analysis, 1999 (Beijing university of aeronautics and astronautics press).

[5] Zhu, Q. M., Warwick, K. and Douce, J. L.: Adaptive general predictive controller for nonlinear systems. IEE proc. -Control Theory Applic. 1991, 138(1), 33-40.

DOI: 10.1049/ip-d.1991.0005

[6] 6 Butt, N. R. and Shafiq, M.: Adaptive tracking of non-linear dynamic plants, using the U-model. IMechE, J. Systems and Control Engineering, 2006, 220(16), 473-487.

DOI: 10.1243/09596518jsce190

[7] 7 Shafiq, M. and Haseebuddin, M.: U-model-based internal model control for non-linear dynamic plants. IMechE, J. Systems and Control Engineering, 2005, 219(6), 449-458.

DOI: 10.1243/095965105x33563

[8] 8 Shafiq, M. and Khan, T.: Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plant. 1st International Systems and control in Aerospace and Atronautics, China, Feb. (2006).

DOI: 10.1109/isscaa.2006.1627534