Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Indirect Adaptive Control for Discrete-Time Nonlinear Systems based on T-S Fuzzy Model

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Khouloud Elloumi, Mohamed Jemel, Mohamed Chtourou
10.5120/ijca2016910357

Khouloud Elloumi, Mohamed Jemel and Mohamed Chtourou. Indirect Adaptive Control for Discrete-Time Nonlinear Systems based on T-S Fuzzy Model. International Journal of Computer Applications 143(9):43-49, June 2016. BibTeX

@article{10.5120/ijca2016910357,
	author = {Khouloud Elloumi and Mohamed Jemel and Mohamed Chtourou},
	title = {Indirect Adaptive Control for Discrete-Time Nonlinear Systems based on T-S Fuzzy Model},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {143},
	number = {9},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {43-49},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume143/number9/25109-2016910357},
	doi = {10.5120/ijca2016910357},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The main goal of this paper is to present an indirect adaptive fuzzy control of discrete-time non affine nonlinear systems with parametric variations. The synthesis of the state feedback control law is based on the Takagi-Sugeno (T-S) fuzzy models developed by a local description of the considered system. In the first step, the model parameters locally estimated by the fuzzy model are adjusted using gradient method. In the second step, the local control gain based on pole placement is computed. After that, the global state feedback control law is applied to the nonlinear system. Based on the Lyapunov stability theory, the asymptotic stability of the proposed state feedback adaptive fuzzy control method is studied to ensure the global stability of the system. To illustrate the performance of the proposed controller, inverted pendulum and two links robot manipulator arm are presented.

References

  1. Khalil, H.K. 2015. Nonlinear Control, 1st edition 07458. Pearson education, Prentice Hall.
  2. Boulkroune, A. and al. 2014. Indirect adaptive fuzzy control scheme based on observer for nonlinear systems:A novel SPR-filter approach, Neurocomputin.
  3. Slotine, J. J. and Li, W. 1991. Applied Nonlinear Control, Prentice Hall Englewood Cliffs, New Jersey.
  4. Tanaka, K. and Wang, H. O. 2001. Fuzzy Control Systems Design and Analysis, John Wiley & Sons.
  5. Benhlima, D. 2009. “Méthodologies de commande Floue adaptative de certaines classes de systèmes non linéaires”. Doctoral thesis, Sfax Engineering School, Sfax, Tunisia.
  6. Moraire, Y. 2001. “Mise en oeuvre de lois de commande pour les modèles flous de type Takagi-Sugeno”. Doctoral thesis, University of Valenciennes and du Hainaut Cambrésis.
  7. Su, J. P. and al. 2001. Adaptive fuzzy sliding mode control with GA-based reaching laws. Fuzzy Sets and Systems, vol. 120, pp. 145-158.
  8. Ratmüller, M. and Murgaš, J. 2004. Fuzzy modelling and adaptive control of uncertain system. Journal of Electrical Engineering, vol. 55, no. 9-10, pp. 251-255.
  9. Wang, L.X. 2002. Fuzzy basis function, universal approximation and orthogonal least squares learning. IEEE Transaction on Neural Networks. Vol. 3. Pp. 807-814.
  10. Wang, L.X. 1996. Stable adaptive fuzzy controller with application to inverted pendulum tracking. IEEE transaction on systems, Man and Cybermetics. Vol. 26. Pp 677-69.

Keywords

Indirect adaptive control, T-S fuzzy model, Discrete-time nonlinear systems, Stability analysis.