Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2009 | 19 | 4 | 619-630

Tytuł artykułu

Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control

Treść / Zawartość

Warianty tytułu

Języki publikacji



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.








Opis fizyczny




  • College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China
  • Department of Electronic, Electrical and Computer Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
  • Department of Control Systems Engineering, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-952 Gdańsk, Poland


  • 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.
  • Bezdek, J. (1974). Comparing different approaches to model error modeling in robust identification, Journal of Cybernetics 3(3): 58-71.
  • 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.
  • Chien, C.-J., C.-T. H. and Yao, C.-Y. (2004). Fuzzy systembased adaptive iterative learning control for nonlinear plants with initial state errors, IEEE Transactions on Fuzzy Systems 12(5): 724-732.
  • Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation, International Journal of Fuzzy Systems 2: 267-278.
  • 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.
  • 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.
  • 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.
  • 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.
  • Ogata, K. (1995). Discrete-time Control System, 2nd Ed., Prentice-Hall, Upper Saddle River, NJ.
  • 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.
  • 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.
  • 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.
  • Wang, L. (1994). Adaptive Fuzzy System and Control: Design and Stability Analysis, Prentice-Hall, Englewood Cliffs, NJ.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.