Relaxed LMIs observer-based controller design via improved t-s fuzzy model structure

Authors:
Wei Xie;Huaiyu Wu;Xin Zhao
Affiliations:
Satellite Venture Business Laboratory, Kitami Institute of Technology, Hokkaido, Japan;College of Information Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei Province, P. R. China;College of Information Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei Province, P. R. China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

Citing 9
Cited 0

Stability analysis and design of fuzzy control systems

Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
A general fuzzy scheme for nonlinear processes with stability analysis

Fuzzy Sets and Systems
Control laws for Takagi—Sugeno fuzzy models

Fuzzy Sets and Systems
Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities

IEEE Transactions on Fuzzy Systems
An approach to fuzzy control of nonlinear systems: stability and design issues

IEEE Transactions on Fuzzy Systems
Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs

IEEE Transactions on Fuzzy Systems
Stabilizing controller design for uncertain nonlinear systems using fuzzy models

IEEE Transactions on Fuzzy Systems
New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Relaxed linear matrix inequalities (LMIs) conditions for fuzzy observer-based controller design are proposed based on a kind of improved T-S fuzzy model structure. The improved structure included the original T-S fuzzy model and enough large bandwidth pre- and post-filters. By this structure fuzzy observer-based controller design can be transformed into LMIs optimization problem. Compared with earlier results, it includes the less number of LMIs that equals the number of fuzzy rules plus one positive definition constraint of Lyapunov function. Therefore, it provides us with less conservative results for fuzzy observer-based controller design. Finally, a numerical example is demonstrated to show the efficiency of proposed method.