Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2003-02, Vol.11 (1), p.57-67
Main Authors: Cao, Yong-Yan, Lin, Zongli
Format: Article
Language:English
Subjects:
Actuators
Control system analysis
Control systems
Dynamical systems
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Mathematical models
Nonlinear control systems
Nonlinear dynamics
Nonlinear systems
Representations
Robust stability
State feedback
Studies
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Summary:Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2002.806317