Adaptive prediction and control of discrete-time Takagi-Sugeno fuzzy systems

被引:4
作者
Qi, Ruiyun [2 ]
Tao, Gang [1 ,2 ]
Tan, Chang [2 ]
Yao, Xuelian [2 ]
机构
[1] Univ Virginia, Dept Elect & Comp Engn, Charlottesville, VA 22903 USA
[2] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
adaptive control; fuzzy systems; output tracking; parameter uncertainties; prediction model; SUFFICIENT CONDITIONS; STABILITY ANALYSIS; MODEL; DESIGN;
D O I
10.1002/acs.2298
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper derives a new prediction model of the global discrete-time input-output multiple-delay TakagiSugeno (T-S) fuzzy systems with multiple delays and employs it for adaptive fuzzy control in the presence of system parameter uncertainties. On the basis of 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 on the basis of an error model resulting 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. Copyright (c) 2012 John Wiley & Sons, Ltd.
引用
收藏
页码:560 / 575
页数:16
相关论文
共 26 条
[1]  
[Anonymous], 2010, Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach
[2]   Adaptive TS-FNN control for a class of uncertain multi-time-delay systems: The exponentially stable sliding mode-based approach [J].
Chiang, Tung-Sheng ;
Chiu, Chian-Song ;
Liu, Peter .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2009, 23 (04) :378-399
[3]   An indirect model reference adaptive fuzzy control for SISO Takagi-Sugeno model [J].
Cho, YW ;
Park, CW ;
Park, M .
FUZZY SETS AND SYSTEMS, 2002, 131 (02) :197-215
[4]   Stability analysis of the discrete Takagi-Sugeno fuzzy model with time-varying consequent uncertainties [J].
Chou, JH ;
Chen, SH .
FUZZY SETS AND SYSTEMS, 2001, 118 (02) :271-279
[5]  
Farrell J. A., 2006, Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches
[6]  
Goodwin G. C., 1984, Adaptive filtering prediction and control
[7]   The design of TSK-type fuzzy controllers using a new hybrid learning approach [J].
Lin, CJ ;
Xu, YJ .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2006, 20 (01) :1-25
[8]   Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems [J].
Nounou, HN ;
Passino, KA .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2004, 12 (01) :70-83
[9]   Sufficient conditions for the stability of linear Takagi-Sugeno free fuzzy systems [J].
Pang, CT ;
Guu, SM .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2003, 11 (05) :695-700
[10]   T-S model based indirect adaptive fuzzy control using online parameter estimation [J].
Park, CW ;
Cho, YW .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (06) :2293-2302