Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control
International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 4, pp. 619-630.

Voir la notice de l'article provenant de la source Library of Science

In this paper, a unified nonlinear modeling and control scheme is presented. A self-structuring Takagi-Sugeno (T-S) fuzzy model is used to approximate the unknown nonlinear plant based on I/O data collected on-line. Both the structure and the parameters of the T-S fuzzy model are updated by an on-line clustering method and a recursive least squares estimation (RLSE) algorithm. The rules of the fuzzy model can be added, replaced or deleted on-line to allow a more flexible and compact model structure. The overall controller consists of an indirect adaptive controller and a supervisory controller. The former is the dominant controller, which maintains the closed-loop stability when the fuzzy system is a good approximation of the nonlinear plant. The latter is an auxiliary controller, which is activated when the tracking error reaches the boundary of a predefined constraint set. It is proven that global stability of the closed-loop system is guaranteed in the sense that all the closed-loop signals are bounded and simulation examples demonstrate the effectiveness of the proposed control scheme.
Keywords: fuzzy control, self-structuring fuzzy model, on-line modeling, stability
Mots-clés : sterowanie rozmyte, model rozmyty, stabilność
@article{IJAMCS_2009_19_4_a8,
     author = {Qi, R. and Brdy\'s, M. A.},
     title = {Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {619--630},
     publisher = {mathdoc},
     volume = {19},
     number = {4},
     year = {2009},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_4_a8/}
}
TY  - JOUR
AU  - Qi, R.
AU  - Brdyś, M. A.
TI  - Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2009
SP  - 619
EP  - 630
VL  - 19
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_4_a8/
LA  - en
ID  - IJAMCS_2009_19_4_a8
ER  - 
%0 Journal Article
%A Qi, R.
%A Brdyś, M. A.
%T Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control
%J International Journal of Applied Mathematics and Computer Science
%D 2009
%P 619-630
%V 19
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_4_a8/
%G en
%F IJAMCS_2009_19_4_a8
Qi, R.; Brdyś, M. A. Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control. International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 4, pp. 619-630. http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_4_a8/

[1] Angelov, P. P. and Filev, D. P. (2004). An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics 34(1): 484-498.

[2] Bezdek, J. (1974). Comparing different approaches to model error modeling in robust identification, Journal of Cybernetics 3(3): 58-71.

[3] Chen, F. and Khalil, H. (1995). Adaptive control of a class of nonlinear discrete-time systems using neural networks, IEEE Transactions on Automatic Control 40(5): 791-801.

[4] Chien, C.-J., C.-T. H. and Yao, C.-Y. (2004). Fuzzy system based adaptive iterative learning control for nonlinear plants with initial state errors, IEEE Transactions on Fuzzy Systems 12(5): 724-732.

[5] Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation, International Journal of Fuzzy Systems 2: 267-278.

[6] Gao, Y. and Er, M. J. (2003). Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems, IEEE Transactions on Fuzzy Systems 11(4): 462-477.

[7] Gustafson, D. E. and Kessel, W. C. (1979). Global random optimization by simultaneous perturbation stochastic approximation, Proceedings of the IEEE Control Decision Conference, San Diego, CA, USA, pp. 761-766.

[8] Hao, Y. (1998). General SISO Takagi-Sugeno fuzzy system with linear rule consequent are universal approximators, IEEE Transactions on Fuzzy Systems 6(4): 582-587.

[9] Hao, Y., Y. D.-S. L. and Shao, S. (1999). Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators, IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics 29(5): 508-514.

[10] Ogata, K. (1995). Discrete-time Control System, 2nd Ed., Prentice-Hall, Upper Saddle River, NJ.

[11] Park, C.-W. and Cho, Y.-W. (2004). T-S model based indirect adaptive fuzzy control using online parameter estimation, IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics 34(6): 2293-2302.

[12] Phan, P. A. and Gale, T. J. (2008). Direct adaptive fuzzy control with a self-structuring algorithm, Fuzzy Sets and Systems 159(8): 871-899.

[13] Qi, R. and Brdys, M. A. (2008). Stable indirect adaptive control based on discrete-time T-S fuzzy model, Fuzzy Sets and Systems 159(8): 900-925.

[14] Wang, L. (1994). Adaptive Fuzzy System and Control: Design and Stability Analysis, Prentice-Hall, Englewood Cliffs, NJ.