State-Space Recurrent Fuzzy Neural Networks for Nonlinear System Identification
Neural Processing Letters
Recurrent Fuzzy CMAC for Nonlinear System Modeling
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Mean-based fuzzy identifier and control of uncertain nonlinear systems
Fuzzy Sets and Systems
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In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system to the specified regions. Moreover, a variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances, and modeling errors. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper