Adaptive fuzzy T-S control based on local linear integral controllers
Applications of Large Scale Industrial Systems, Volume # 1 | Part# 1
Authors
Ruiyun Qi; Mietek A. Brdys
Digital Object Identifier (DOI)
10.3182/20060830-2-SF-4903.00025
Page Numbers:
137-142
Index Terms
indirect adaptive control,fuzzy control,Takagi-Sugeno (T-S) models,local linear controller
Abstract
A Takagi-Sugeno (T-S) fuzzy model based adaptive control algorithm for a class of multiple-input-multiple-output (MIMO) nonlinear uncertain systems is presented in this paper. The T-S model consists of a set of linear local models which can be considered as the linearization models of the nonlinear systems in different operating regions. Local integral controllers are designed based on local linear fuzzy models and combined to generate the overall controller using fuzzy weighted integration. The consequent parameters of the T-S model can be updated online in the presence of external disturbances and parameter perturbations. Stability analysis shows all the signals in the closed-loop system are bounded. Simulation results on tracking control of a two-link robot manipulator are given to illustrate the effectiveness of the proposed method.
References
[1] B. Kosko (1994), "Fuzzy systems as universal
approximtors," IEEE Trans. Computers, vol. 43,
pp. 1329-1333.
[2] R. Rovatti (1998), "Fuzzy piecewise multilinear and
piecewise linear systems as universal
approximators in Sobolev norm," IEEE Trans.
Fuzzy Syst., vol. 6, pp. 235-249.
[3] M. A. Brdys and G. J. Kulawski (1999), "Dynamic
neural controllers for indunction motor," IEEE
Trans. Neural Networks, vol. 10, no. 2, pp. 340-
355.
[4] G. J. Kulawski and M. A. Brdys (2000), "Stable
adaptive control with recurrent networks,"
Automatica, vol. 36, pp. 5-22.
[5] Z.-P. Jiang and Y. Wang (2001), "Input-to-state
stability for discrete-time nonlinear systems,"
Automatica, vol. 37, pp. 857-869.
[6] Y. Jin (2003), Advanced fuzzy systems design and
applications. Heidelberg, NY: Physica-Verl.
[7] G. Tao (2003), Adaptive design and analysis.
Hoboken, NJ: John Wiley & Sons, Inc.
[8] Q. Sun, R. Li and P. Zhang (2003), "Stable and
optimal adaptive fuzzy control of complex
systems using fuzzy dynamic model," Fuzz. Sets
and Syst., vol 133, pp. 1-17.
[9] C.-W. Park and Y.-W. Cho (2004), "T-S Model
Based Indirect Adaptive Fuzzy Control Using
Online Parameter Estimation," IEEE Trans. On
Syst. Man. And Cyber., Part B, Vol. 34, pp.
2293-2302.
[10] N. Li, S.-Y. Li and Y.-G. Xi (2004), "Multi-model
predictive control based on the Takagi-Sugeno
fuzzy models: a case study," IEEE Trans. On
Syst. Man. And Cyber., part B, vol. 34, No. 1, pp.
788-795.
[11] R. Qi and M.A. Brdys (2005), "Adaptive fuzzy
modelling and control for discrete-time
nonlinear uncertain systems," in Proc. Of the
2005 American Control Conference, pp. 1108-
1113.
