Structure identification of fuzzy model
Fuzzy Sets and Systems
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
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
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An approach to systematic design of the fuzzy control system
Fuzzy Sets and Systems
Flexible stability criteria for a linguistic fuzzy dynamic system
Fuzzy Sets and Systems
An adaptive fuzzy controller based on sliding mode for robotmanipulators
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
An adaptive fuzzy sliding mode controller for robotic manipulators
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
Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs
IEEE Transactions on Fuzzy Systems
Decoupled fuzzy sliding-mode control
IEEE Transactions on Fuzzy Systems
Stabilizing fuzzy system models using linear controllers
IEEE Transactions on Fuzzy Systems
New approaches to relaxed quadratic stability condition of fuzzy control systems
IEEE Transactions on Fuzzy Systems
A multiple Lyapunov function approach to stabilization of fuzzy control systems
IEEE Transactions on Fuzzy Systems
International Journal of Automation and Computing
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This paper presents a fuzzy rule-base combined controller, which is a fuzzy rule-based combination of linear and switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties. The switching state-feedback controller is employed to drive the system states toward the origin. When the system state approaches the origin, the linear state-feedback controller will gradually replace the switching state-feedback controller. The smooth transition between the linear and switching state-feedback controllers is governed by the fuzzy rules. By using the fuzzy rule-based combination technique, the proposed fuzzy rule-base combined controller integrates the advantages of both the linear and switching state-feedback controllers but eliminates their disadvantages. As a result, the proposed fuzzy controller provides good performance during the transient period and the chattering effect is removed when the system state approaches the origin. Stability conditions will be derived to guarantee the system stability. Furthermore, a saturation function is employed to replace the switching component to alleviate the chattering during the transient period. By using the proposed fuzzy rule-based combination technique, the steady state error introduced by the saturation function can be eliminated. Application examples will be given to show the merits of the proposed approach.