By Topic

Robust and reliable H fuzzy hyperbolic decentralized control for a class of nonlinear large-scale systems with parametric uncertainties

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Liu Xinrui ; Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang ; Zhang Huaguang ; Lun Shuxian ; Wang Yingchun

This paper develops robust Hinfin fuzzy hyperbolic control for nonlinear large-scale systems with parameter uncertainties. Firstly, fuzzy hyperbolic model (FHM) can be used to establish the model for certain complex large-scale systems, then according to the Lyapunov direct method and the decentralized control theory of large-scale systems, the sufficient condition in the terms of linear matrix inequalities (LMIs) which guarantee the existence of the state feedback Hinfin control based on FHM for the fuzzy large-scale systems is proposed. The main advantage of using FHM over Takagi-Sugeno (T-S) fuzzy model is that no premise structure identification is needed and no completeness design of premise variables space is needed, therefore there needs much less computation expense than that of using T-S fuzzy model, especially when a lot of fuzzy rules are needed to approximate highly nonlinear complex systems. In addition, an FHM is not only a kind of valid global description but also a kind of nonlinear model in nature. A simulation example is provided to illustrate the design procedure of the proposed method and its validity.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008