Nonlinear control systems: an introduction (2nd ed.)
Nonlinear control systems: an introduction (2nd ed.)
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Robust stability analysis and design method for the fuzzy feedback linearization regulator
IEEE Transactions on Fuzzy Systems
An improved stable adaptive fuzzy control method
IEEE Transactions on Fuzzy Systems
The adaptive control of nonlinear systems using the Sugeno-type of fuzzy logic
IEEE Transactions on Fuzzy Systems
Stability Analysis of a Simple-Structured Fuzzy Logic Controller
Journal of Intelligent and Robotic Systems
Stability analysis of the simplest Takagi-Sugeno fuzzy control system using circle criterion
Information Sciences: an International Journal
Information Sciences: an International Journal
An LMI-based controller design of uncertain nonlinear systems using Takagi-Sugeno fuzzy region model
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
CSS'11 Proceedings of the 5th WSEAS international conference on Circuits, systems and signals
Static output feedback for T-S fuzzy model of a synchronuous machine
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
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In order to cope with parametric uncertainties of nonlinear systems, a numerical analysis method of a robust feedback linearization control scheme based on fuzzy models is proposed. The fuzzy feedback linearization control scheme ensures that the control operates stably under bounded parametric uncertainties. For the system with known uncertainty bounds, linear matrix inequality based robust stability condition is derived and a robust fuzzy feedback linearization regulators can be designed by choosing the control parameters satisfying the inequalities which give sufficient conditions for the closed loop system to be stable. To verify the validity and effectiveness of the designed stability analysis methods, the suggested analysis techniques are applied to a balancing control of inverted pendulum systems.