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Reseach Article

Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems

by Imen Zaidi, Mohamed Chtourou, Mohamed Djemel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 10
Year of Publication: 2016
Authors: Imen Zaidi, Mohamed Chtourou, Mohamed Djemel
10.5120/ijca2016910377

Imen Zaidi, Mohamed Chtourou, Mohamed Djemel . Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems. International Journal of Computer Applications. 143, 10 ( Jun 2016), 23-30. DOI=10.5120/ijca2016910377

@article{ 10.5120/ijca2016910377,
author = { Imen Zaidi, Mohamed Chtourou, Mohamed Djemel },
title = { Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 10 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number10/25114-2016910377/ },
doi = { 10.5120/ijca2016910377 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:46:22.871032+05:30
%A Imen Zaidi
%A Mohamed Chtourou
%A Mohamed Djemel
%T Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 10
%P 23-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, three neural control strategies are addressed to a class of single input-single output (SISO) discrete-time nonlinear systems affected by parametric variations. According to the control scheme, in a first step, a direct neural model (DNM) is developed to emulate the behavior of the system, then an inverse neural model (INM) is synthesized using specialized learning technique and cascaded to the system as a controller. The sliding mode backpropagation algorithm (SM-BP), which presents in a previous study robustness and high speed learning, is adopted for the training of the neural models. However, in the presence of strong parametric variations, the synthesized (INM) shows limitations to present satisfactory tracking performances. In fact, in order to improve the control results, two neural control strategies such as hybrid control and neuro-sliding mode control are proposed in this work. A simulation example is treated to show the effectiveness of the proposed control strategies

References
  1. Y-W. Chen, J-B. Yang, C-C. Pan, D-L. Xu, Z-J. Zhou “Identification of uncertain nonlinear systems: Constructing belief rule-based models” Knowledge Based System, vol. 73, pp. 124-133, 2015.
  2. Y. Lin, Y. Shi, R. Burton “Modeling and Robust Discrete-Time Sliding-Mode Control Design for a Fluid Power Electrohydraulic Actuator (EHA) System” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 1-10, 2013
  3. A.aydi, M. Djemal, M. Chtourou “Robust pole assignement for control uncertain nonlinear discrete-time systems” 12th International Multi-Conference on Systems, Signals & Devices, pp. 1-5, 2015.
  4. L.A. Zadeh “Fuzzy Sets” Information and control, vol. 8, pp. 338-353, 1965
  5. C.C. Chuang, J-T. Jeng, C-W. Tao “Hybrid robust approach for TSK fuzzy modeling with outliers” Expert Systems with Applications, vol.36, no. 5, pp. 8925-8931, 2009.
  6. K.S. Narendra, K. Parthasarathy “Identification and control of dynamical systems using neural networks” IEEE Transaction on Neural Networks, vol. 1, no.1, pp. 4–27, 1990.
  7. A.V. Topalov, O. Kaynak “Robust neural identification of robotic manipulators using discrete time adaptive sliding mode learning” In Proceeding of International Federation of Automatic Control World Congress, vol. 38, no.1, pp. 336-341, 2005.
  8. X-H. Ji “Fuzzy neural network control and identification for uncertain nonlinear systems” Chinese Control and Decision Conference, pp.4237-4242, 2010
  9. X, Wang, T.Li, C.L.Philip Chen, B. Lin “Adaptive robust control based on single neural network approximation for a class of uncertain strict-feedback discrete-time nonlinear systems” Neurocomputing, vol. 138, pp. 325-331, 2014.
  10. Z. Wang, D. W. C. Ho, Y. Liu, X. Liu “Robust control for a class of nonlinear discrete delay stochastic systems with missing measurements” Automatica, vol.45, no. 3, pp. 684-691, 2009.
  11. S. Sam Ge, J. Wang “Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients” IEEE Transaction on Automatic Control, vol. 48, no.8, pp. 1463-1469, 2003.
  12. Z. Yan, J. Wang “Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks” IEEE Transactions on Neural Networks and Learning system, vol. 25, no. 3, pp. 457-469, 2014.
  13. H. Abid, M. Chtourou, A. Toumi “Robust fuzzy sliding mode controller for discrete nonlinear systems” International Journal of Computers, Communications & Control vol. 3, no. 1, pp. 6-20, 2008.
  14. F O. Tellez, E N. Sanchez, R G. Hernandez, J A. Ruz-Hernandez, J L. Rullan-Lara “Neural inverse optimal control for discrete time uncertain nonlinear systems stabilization” The International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 2012.
  15. T. Huang “Nonlinear torque and air-to-fuel ratio control of spark ignition engines using neuro-sliding mode techniques” International Journal of Neural Systems, vol. 21, no. 3, pp. 213–224, 2011.
  16. T.A.A. Alzohairy. “Neural internal model control for tracking unknown nonaffine nonlinear discrete-time systems under external disturbances” International Journal of Computer Applications, vol. 40, no.6, pp. 19-26, 2012.
  17. D.E. Rumelhart, G.E. Hinton, R.J. Williams “Learning internal representation by error propagation. Parallel distributed processing: explorations in the microstructure of cognition, MIT Press Cambridge, MA, vol.1, pp. 318-362, 1986.
  18. G.G. Parma, B.R. Menezes, A.P. Barga “Improving backpropagation with sliding mode control” Proceedings of the Vth Brazilian Symposium on Neural Networks. Belo Horizonte, Brazil: IEEE Computer Society Press, pp. 8-13, 1998
  19. Zaidi, M, Chtourou, M, Djemel “Robust neural control of discrete time uncertain nonlinear systems using sliding mode backpropagation training algorithm” submitted to International Journal of Automation and Computing.
  20. G. Parma, B.R. Menezes, A.P. Barga “Neural networks learning with sliding mode control: the sliding mode backpropagation algorithm” International Journal of Neural Networks, vol. 9, pp.187-193, 1999
  21. V.I Utkin. “Sliding modes and their application in variable structure systems” MIR, Moscow, 1978.
  22. S.Z. Sarpturk, Y. Istefanopulos, O. Kaynak “On the stability of discrete time sliding mode control system” IEEE Transaction on Automatic Control, vol. 32, no. 10, pp. 930-932, 1987
  23. D. Psaltis, A.Sideris, A.A. Yamamura “A multilayered neural network controller” IEEE Control Systems Magazine, pp. 17-21, 1988.
  24. K J. Hunt, D. Sbarbaro, R. Zbikowski, P.J. Gawthrop “Neural Networks for Control Systems -A Survey,” Automatica, vol. 28, no. 6, pp. 1083-1112, 1992.
  25. W.T. van Luenen “Neural networks for control: on Knowledge Representation and Learning,” Ph.D Thesis, Control Laboratory of Electrical Engineering, University of Twente,
  26. Enschede, the Netherlands. 1993.
  27. O. Sørensen “Neural networks in control applications” Ph.D. Thesis. Aalborg University, Department of Control Engineering, 1994
  28. Y. Lin, Y. Shi, R. Burton “Modeling and robust discrete-time sliding-mode control design for a fluid power electrohydraulic actuator (EHA) System” IEEE/ASME Transactions on Mechatronics, vol.18, no.1, pp. 1-10, February 2013.
  29. D.M. Tuan, Z. Man, C. Zhang, J. Jin, H. Wang “Robust sliding mode learning control for uncertain discrete-time multi-input multi-output systems” IET Control Theory and Applications, vol. 8, no. 12, pp. 1045-1053, 2014.
  30. F.Huang, Y. Jing, G.M. Dimirovski “ Sliding mode feedback control for uncertain discrete-time Markov Jump systems” Proceedings of the 18th World Congress The International Federation of Automatic Control (IFAC), vol. 4, no. 1, pp. 2419-2423, 2011.
  31. V.I Utkin. “Sliding mode in Control and Optimization”. Springer- Verlag, 1981.
  32. M. Ertugrul, O. Kaynak “Neuro sliding mode control of robotic manipulators”. Mechatronics, vol. 10, no. 12, pp. 243-267, 2000
  33. Ch-H. Tsai, H-Y. Chung, F-M. Yu “Neuro-Sliding mode control with its applications to seesaw systems” IEEE transactions on neural networks, vol. 15, no. 1, pp. 124-134, January 2004.
  34. T. Huang, “Nonlinear torque and air-to fuel ratio control of spark ignition engines using neuro-sliding mode techniques” International Journal of Neural Systems, Vol. 21, No. 3, pp. 213-224, 2011
  35. M. Mihoub, A.S. Nouri, R. B. Abdennour “Real time application of discrete second order sliding mode control to a chemical reactor” Control Engineering Practice, Vol. 17, pp. 1089-1095, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

SISO Discrete-time uncertain nonlinear systems neural modelling sliding mode backpropagation algorithm INM control hybrid control neuro-sliding mode control.