Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
An LMI-based H∞ fuzzy control system design with TS framework
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
Fuzzy modeling and control of a nonlinear magnetic bearing system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
State feedback control of continuous-time T-S fuzzy systems via switched fuzzy controllers
Information Sciences: an International Journal
A general and formal methodology to design stable nonlinear fuzzy control systems
IEEE Transactions on Fuzzy Systems
LMI-based robust flight control of an aircraft subject to CG variation
International Journal of Systems Science
An architecture for adaptive fuzzy control in industrial environments
Computers in Industry
Journal of Control Science and Engineering
Robust h∞ control with pole placement constraints for t-s fuzzy systems
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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In this paper, the synthesis of an Linear Matrix Inequality (LMI)-based stable fuzzy control system with pole-placement constraint is presented. The requirements of stability and pole-placement region are formulated based on the Lyapunov direct method. By recasting these constraints into LMIs, we formulate an LMI feasibility problem for the design of the fuzzy state feedback control system that guarantees stability and satisfies desired transient responses. This theoretical approach is applied to a nonlinear magnetic bearing system concerning the issue of rotor position control. Simulation results show that the proposed LMI-based design methodology yields better performance than those of a linear local controller or single objective controller. In addition, it is observed that the proposed fuzzy state feedback controller provides superior stability robustness against parameter variations.