Constrained Infinite-Horizon Model Predictive Control for Fuzzy-Discrete-Time Systems

被引：84
作者：
Xia, Yuanqing [1 ]
Yang, Hongjiu [1 ]
Shi, Peng [2 ,3 ]
Fu, Mengyin [1 ]
机构：
[1] Beijing Inst Technol, Dept Automat Control, Beijing 100081, Peoples R China
[2] Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales
[3] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2010年 / 18卷 / 02期
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
Discrete-time system; linear-matrix inequality (LMI); model-predictive control (MPC); parallel-distributed compensation (PDC); Takagi-Sugeno (T-S) fuzzy system; NONLINEAR-SYSTEMS; STABILIZATION CONDITIONS; ROBUST STABILIZATION;
D O I：
10.1109/TFUZZ.2010.2043441
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The problem of constrained infinite-horizon model-predictive control for fuzzy-discrete systems is considered in this paper. New sufficient conditions are proposed in terms of linear-matrix inequalities. Based on the optimal solutions of these sufficient conditions at each sampling instant, we design both parallel-distributed compensation and nonparallel-distributed compensation state-feedback controllers, which can guarantee that the resulting closed-loop fuzzy-discrete system is asymptotically stable. In addition, the fuzzy-feedback controllers meet the specifications for the fuzzy-discrete systems with both input and output constraints. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques.
引用
收藏
页码：429 / 436
页数：8
相关论文
共 29 条
[1]  
de Oliveira M. C., 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), P3611, DOI 10.1109/CDC.1999.827913
[2]   Further studies on LMI-based relaxed stabilization conditions for nonlinear systems in Takagi-Sugeno's form [J].
Ding, BC ;
Sun, HX ;
Yang, P .
AUTOMATICA, 2006, 42 (03) :503-508
[3]  
Filali Salim, 2007, ICIC Express Letters, V1, P197
[4]   Improved H∞ control of discrete-time fuzzy systems:: a cone complementarity linearization approach [J].
Gao, HJ ;
Wang, ZD ;
Wang, CH .
INFORMATION SCIENCES, 2005, 175 (1-2) :57-77
[5]   Stabilization of Nonlinear systems under variable sampling: A fuzzy control approach [J].
Gao, Huijun ;
Chen, Tongwen .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2007, 15 (05) :972-983
[6]  
Gong C, 2009, INT J INNOV COMPUT I, V5, P1429
[7]   LMI-based relaxed nonquadratic stabilization conditions for nonlinear systems in the Takagi-Sugeno's form [J].
Guerra, TM ;
Vermeiren, L .
AUTOMATICA, 2004, 40 (05) :823-829
[8]   Robust digital model predictive control for linear uncertain systems with saturations [J].
Hu, LS ;
Huang, BA ;
Cao, YY .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2004, 49 (05) :792-796
[9]   Fuzzy model predictive control [J].
Huang, Y.L., 2000, IEEE, Piscataway, NJ, United States (08) :665-678
[10]   Robust constrained model predictive control using linear matrix inequalities [J].
Kothare, MV ;
Balakrishnan, V ;
Morari, M .
AUTOMATICA, 1996, 32 (10) :1361-1379
123