Adaptive control of discrete-time T-S fuzzy systems with multiple input-output delays

This paper derives a new prediction model of global discrete-time input-output multiple-delay Takagi-Sugeno (T-S) fuzzy systems with multiple delays and employs it for adaptive fuzzy control in the presence of system parameter uncertainties. Based on a model-based approach, a new system parametrization and adaptive control scheme are developed with detailed design procedure and complete stability analysis. The derived new fuzzy prediction model involves not only the current values of the membership functions but also their past values, expanding its capacity of approximating dynamic systems. A stable adaptive law is developed based on an error model resulted from a new augmented parametric model for which a signal bounding property is also proved, crucial for closed-loop system stability. An illustrative example is presented to demonstrate the studied new concepts and to verify the desired performance of the new types of adaptive fuzzy control systems.

[1]  Kazuo Tanaka,et al.  Stability analysis and design of fuzzy control systems , 1992 .

[2]  Frank L. Lewis,et al.  Adaptive Approximation Based Control-Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches-[Book review; J. A. Farrell and M. M. Polycarpou] , 2007 .

[3]  Kazuo Tanaka,et al.  Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities , 1996, IEEE Trans. Fuzzy Syst..

[4]  Ruiyun Qi,et al.  Stable indirect adaptive control based on discrete-time T-S fuzzy model , 2008, Fuzzy Sets Syst..

[5]  Dimitris C. Theodoridis,et al.  Indirect Adaptive Control of Unknown Multi Variable nonlinear Systems with Parametric and Dynamic Uncertainties Using a New Neuro-Fuzzy System Description , 2010, Int. J. Neural Syst..

[6]  Gang Tao,et al.  Adaptive Control Design and Analysis , 2003 .

[7]  Chian-Song Chiu,et al.  Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach , 2009 .

[8]  Young-Wan Cho,et al.  T-S model based indirect adaptive fuzzy control using online parameter estimation , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[10]  Wen-June Wang,et al.  An improved stability criterion for T-S fuzzy discrete systems via vertex expression , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Mignon Park,et al.  An indirect model reference adaptive fuzzy control for SISO Takagi-Sugeno model , 2001, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[12]  Ruiyun Qi,et al.  Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control , 2009, Int. J. Appl. Math. Comput. Sci..

[13]  Farid Sheikholeslam,et al.  Stability analysis and design of fuzzy control systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[14]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[15]  Chin-Tzong Pang,et al.  Sufficient conditions for the stability of linear Takagi-Sugeno free fuzzy systems , 2003, IEEE Trans. Fuzzy Syst..

[16]  Hao Ying,et al.  Sufficient conditions on uniform approximation of multivariate functions by general Takagi-Sugeno fuzzy systems with linear rule consequent , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Min Tan,et al.  Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach , 2008, IEEE Transactions on Fuzzy Systems.

[18]  Guang Ren,et al.  Stability analysis and systematic design of Takagi-Sugeno fuzzy control systems , 2005, Fuzzy Sets Syst..

[19]  Yih-Guang Leu,et al.  Adaptive T-S fuzzy-neural modeling and control for general MIMO unknown nonaffine nonlinear systems using projection update laws , 2010, Autom..

[20]  Dimitris C. Theodoridis,et al.  A New Adaptive Neuro-Fuzzy Controller for Trajectory Tracking of Robot Manipulators , 2011, Int. J. Robotics Autom..

[21]  Jyh-Horng Chou,et al.  Stability analysis of the discrete Takagi-Sugeno fuzzy model with time-varying consequent uncertainties , 2001, Fuzzy Sets Syst..