Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Robust adaptive control
Stable indirect fuzzy adaptive control
Fuzzy Sets and Systems - Theme: Modeling and control
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
T-S model based indirect adaptive fuzzy control using online parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Universal fuzzy controllers based on generalized T--S fuzzy models
Fuzzy Sets and Systems
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A mean-based adaptive fuzzy control scheme with state estimation performance is proposed for a class of uncertain nonlinear systems in the presence of only output measurement. In the control scheme, a mean-based fuzzy identifier without prior knowledge of membership functions is merged into direct adaptive controller with a linear state estimator. The structure of the mean-based fuzzy identifier is nonlinear in the adjusted parameters in order to diminish the unfavorable influence of initially designing membership functions on control performance. For the nonlinear structure, a mean method is used to derive adaptive laws. Compared with conventional methods, the advantage of the mean method is that the computation burden can be effectively alleviated because finding the derivative of fuzzy systems is not required. In addition, for the linear state estimator, the state estimation performance with beforehand given attenuation parameter is established by the design of a compensative controller. Finally, two examples are provided to demonstrate the applicability of the proposed scheme.