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2011 | 21 | 4 | 685-696
Tytuł artykułu

Adaptive control scheme based on the least squares support vector machine network

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
Rocznik
Tom
21
Numer
4
Strony
685-696
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-12-21
poprawiono
2011-05-04
poprawiono
2011-07-08
Twórcy
  • Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, El-Menoufiya, Egypt
Bibliografia
  • Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function-Algorithms, Plenum Press, New York, NY.
  • Faanes, A. and Skogestad, S. (2004). pH-neutralization: Integrated process and control design, Computers and Chemical Engineering 28(8): 1475-1487.
  • Ge, S.S. and Wang, C. (2004). Adaptive neural control of uncertain MIMO nonlinear systems, IEEE Transactions on Neural Networks 15(3): 674-692.
  • Ge, S.S., Yang, C. and Lee, T.H. (2008). Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time, IEEE Transactions on Neural Networks 19(9): 1599-1614.
  • Henson, M.A. and Seborg, D.E. (1994). Adaptive nonlinear control of a pH neutralization process, IEEE Transactions on Control System Technology 2(3): 169-182.
  • Huicheng, W.L.L. and Taiyi, Z. (2008). An improved algorithm on least squares support vector machines, Information Technology Journal 7(2): 370-373.
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  • Li-Juan, L., Hong-Y, S. and Jian, C. (2007). Generalized predictive control with online least squares support machines, Acta Automatica Sinica 33(11): pp. 1182-1188.
  • Li, X., Yi Cao, G. and Jian Zhu, X. (2006). Modeling and control of PEMFC based on least squares support vector machines, Energy Conversion and Management 47(7-8): 1032-1050.
  • Mahmoud, T.A. (2010). Multi resolution wavelet least squares support vector machine network for nonlinear system modeling, Proceedings of the 15th International Conference in Automation and Robotics MMAR2010, Międzyzdroje, Poland, pp. 405-410.
  • Nie, J., Loh, A. and Hang, C. (1996). Modeling pH neutralization processes using fuzzy-neural approaches, Fuzzy Sets and Systems 78(1): 5-22.
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  • Robert, M.S. and Jean-Jacques, E.S. (1992). Gaussian networks for direct adaptive control, IEEE Transactions on Neural Networks 3(6): 837-863.
  • Saunders, C., Gammerman, A. and Vovk, V. (1998). Ridge regression learning algorithm in dual variables, Proceedings of the 15th International Conference on Machine Learning ICML-98, Madison, WI, USA, pp. 515-521.
  • Suykens, J.A.K. (2001). Nonlinear modeling and support vector machines, IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary, pp. 287-294.
  • Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B. and Vandewalle, J. (2002). Least Squares Support Vector Machines, World Scientific, Singapore.
  • Suykens, J.A.K. and Vandewalle, J. (1999). Least squares support vector machine classifiers, Neural Processing Letters 9(3): 293-300.
  • Suykens, J.A.K., Vandewalle, J. and Moor, B.D. (2001). Optimal control by least squares support vector machines, Neural Networks 14: 23-35.
  • Vapnik, V. (1998). Statistical Learning Theory, John Wiley, New York, NY.
  • Wang, Y.-N. and Yuan, X.-F. (2008). SVM approximate-based internal model control strategy, Acta Automatica Sinica 34(2): 696-702.
  • Wang, Y., Rong, G. and Wang, S. (2002). Hybrid fuzzy modeling of chemical processes, Fuzzy Sets and Systems 130 (2-1): 265-275.
  • Yoo, S.J., Park, J.B., and Choi, Y.H. (2005). Stable predictive control of chaotic systems using self-recurrent wavelet neural networks, International Journal of Control, Automation, and Systems 3(1): 43-55.
  • Zhang, L., Zhou, W. and Jiao, L. (2004). Wavelet support vector machine, IEEE Transactions on Systems Man, and Cybernetics 34(1): 34-39.
  • Zhang, R. and Wang, S. (2008). Support vector machine based predictive functional control design for output temperature of coking furnace, Journal of Process Control 18(5): 439-448.
  • Zhang, X.-G., Gao, D., Zhang, X.-G. and Ren, S.S. (2005). Robust wavelet support vector machine for regression estimation, International Journal of Information Technology 11(9): 35-45.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i4p685bwm
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