Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A model reference control structure using a fuzzy neural network
Fuzzy Sets and Systems
Design of self-learning fuzzy sliding mode controllers based on genetic algorithms
Fuzzy Sets and Systems
The fuzzy neural network approximation lemma
Fuzzy Sets and Systems
On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decoupled fuzzy sliding-mode control
IEEE Transactions on Fuzzy Systems
Fuzzy control of a benchmark problem: a computing with words approach
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
Fuzzy qualitative trigonometry
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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In this paper, a decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems is presented. To divided into two subsystems to achieve asymptotic stability by decoupled method for a class of fourth-order nonlinear system. The fuzzy-neural network (FNN) is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the FNN controller. A tuning methodology is derived to update weight parts of the FNN. Using Lyapunov law, we derive the decoupled sliding-mode control law and the related parameters adaptive law of FNN. Finally, the decoupled sliding-mode with fuzzy-neural network control (DSMFNNC) is used to control three highly nonlinear systems and confirms the validity of the proposed approach. The method can control one-input and multi-output nonlinear systems efficiently. Using this approach, the response of system will converge faster than that of previous reports.