CFP last date
20 May 2024
Reseach Article

Adaptive Neural Network Control for a Class of MIMO Uncertain Pure-Feedback Nonlinear Systems

by Zhenfeng Chen, Zhongsheng Wang, Jian Cen
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 16
Year of Publication: 2015
Authors: Zhenfeng Chen, Zhongsheng Wang, Jian Cen
10.5120/ijca2015905776

Zhenfeng Chen, Zhongsheng Wang, Jian Cen . Adaptive Neural Network Control for a Class of MIMO Uncertain Pure-Feedback Nonlinear Systems. International Journal of Computer Applications. 124, 16 ( August 2015), 1-5. DOI=10.5120/ijca2015905776

@article{ 10.5120/ijca2015905776,
author = { Zhenfeng Chen, Zhongsheng Wang, Jian Cen },
title = { Adaptive Neural Network Control for a Class of MIMO Uncertain Pure-Feedback Nonlinear Systems },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 16 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number16/22192-2015905776/ },
doi = { 10.5120/ijca2015905776 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:33.328890+05:30
%A Zhenfeng Chen
%A Zhongsheng Wang
%A Jian Cen
%T Adaptive Neural Network Control for a Class of MIMO Uncertain Pure-Feedback Nonlinear Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 16
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neural network (NN), robust adaptive control is synthesized by employing a single NN to approximate the lumped uncertain nonlinearities. The proposed control can eliminate the circularity problem completely, and guarantees semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop and convergence of the tracking error to an arbitrarily small residual set.

References
  1. Ge, S. S., Hang, C. C., Zhang, T. 1998. Nonlinear adaptive control using neural networks and its application to CSTR systems. Journal of Process Control. 9, 313-323.
  2. Shiriaev, A. S., Ludvigsen, H., Egeland, O., Fradkov, A. L. 1999. Swinging up of non-affine in control pendulum. In: In Proceedings of American Control Conference, San Diego, California, USA, 4039-4044.
  3. Hsu, C. T., Chen, S. L. 2003. Nonlinear control of a 3-pole active magnetic bearing system. Automatica. 39, 291-298.
  4. Chen Z. F., Ge S. S., Zhang Y., Li Y. 2014. Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure. IEEE Transactions on Neural Networks and Learning Systems. 25(11), 2017-2029.
  5. Chen, Z. F.,Wang, Z. S., Cen, J. 2015. Output feedback stabilization of a class of non-affine nonlinear systems in discrete time. Internatinal Journal of Computer Applications. 119(16), 1-5.
  6. Park, J. H., Kim, S. H. 2004. Direct adaptive output-feedback fuzzy controller for a nonaffine nonlinear system. IEE Proceedings Control Theory and Applications. 51, 65–72.
  7. Chen, Z. F., Zhang, X. H., Wang, Z. S. 2015. Direct adaptive control for a class of uncertain nonlinear systems. 119(16), 11-15.
  8. Goh, C. J. 1994. Model reference control of nonlinear systems via implicit funcion emulation. International Journal of Control. 60, 91-115.
  9. Goh, C. J., Lee, T. H. 1994. Direct adaptive control of nonlinear systems via implicit funcion emulation. Control-Theory and Advance Technology. 10 (3), 539-552.
  10. Calise, A. J., Hovakimyan, N., Idan, M. 2001. Adaptive output feedback control of nonlinear systems using neural networks. Automatica. 37, 1201-1211.
  11. Hovakimyan, N., Nardi, F. and Calise, A. J. 2002. A novel error boserver-based adaptive output feedback aproach for control of uncertain systems. IEEE Transactions on Automatic Control. 47 (8), 1310-1314.
  12. Polycarpou M. M., Ioannou P. A. 1996. A robust adaptive nonlinear control design, Automatica. 32(3), 423-427.
  13. Jiang Z. P. and Praly L. 1998. Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties. Automatica. 34, 825-840.
  14. Jiang Z. P., Hill D. J. 1999. A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics. IEEE Trans. Automat. Contr. 44, 1705-1711.
  15. Yao B. and Tomizuka M. 1997. Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form. Automatica. 33, 893-900.
  16. Ge S. S., Wang J. 2002. Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems. IEEE Transations on Neural Network, 13(6), 1409-1417.
  17. Tong S., Li Y., Shi P. 2012. Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems, IEEE Transactions on Automatic Control. 47 (8), 1310-1314.
  18. Yesidirek A.,Lewis F. L. 1995. Feedback linearization using neural networks. Automatica. 31(11), 1659-1664.
  19. Girosi F. and Poggio T. 1989. Networks and the best approximation property. Artif. Intell. Lab. Memo. 1164, Mass. Inst. Technol., Cambridge, MA.
  20. Poggio T. and Girosi F. 1990. Networks for approximation and learning. Proc. IEEE, 78, 1481-1497.
  21. Kurdila A. J., Narcowich F. J., Ward J. D. 2001. Persistency of excitation in identification using neural networks. SIAM Journal of Control and Optimization. 33(2), 625-642.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive control neural network control multi-input/multi-output (MIMO) nonlinear systems backstepping