Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System

被引:1
作者
Li Yi-Min [1 ]
Yue Yang [1 ]
Li Li [2 ]
机构
[1] Jiangsu Univ, Fac Sci, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Sch Comp Sci Telecommun Engn, Zhenjiang 212013, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
NONLINEAR-SYSTEMS; NEURAL-CONTROL; TRACKING CONTROL; LOGIC SYSTEMS; DESIGN; SCHEME; SETS;
D O I
10.1155/2012/658424
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type-2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed-loop system and ensure all the signals bounded. Compared with existing Type-1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper.
引用
收藏
页数:27
相关论文
共 56 条
  • [21] Type-2 fuzzy logic systems
    Karnik, NN
    Mendel, JM
    Liang, QL
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1999, 7 (06) : 643 - 658
  • [22] Karnik NN, 1998, 1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2, P915, DOI 10.1109/FUZZY.1998.686240
  • [23] Liang QL, 2000, INT J INTELL SYST, V15, P939, DOI 10.1002/1098-111X(200010)15:10<939::AID-INT3>3.0.CO
  • [24] 2-G
  • [25] Interval type-2 fuzzy logic systems: Theory and design
    Liang, QL
    Mendel, JM
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2000, 8 (05) : 535 - 550
  • [26] Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network
    Lin, Faa-Jeng
    Chou, Po-Huan
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2009, 56 (01) : 178 - 193
  • [27] Type-2 fuzzy controller design using a sliding-mode approach for application to DC-DC converters
    Lin, PZ
    Lin, CM
    Hsu, CF
    Lee, TT
    [J]. IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 2005, 152 (06): : 1482 - 1488
  • [28] Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems
    Lin, Tsung-Chih
    [J]. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2010, 15 (12) : 4084 - 4099
  • [29] Lin TC, 2010, INT J INNOV COMPUT I, V6, P941
  • [30] Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems
    Lin, Tsung-Chih
    Liu, Han-Leih
    Kuo, Ming-Jen
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2009, 22 (03) : 420 - 430