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This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions...
Based on neural networks, an adaptive fast terminal sliding mode (FTSM) control strategy is presented for a class of high-order uncertain nonlinear system. The radial basis function (RBF) neural network is used to online approach uncertainties of system. The mathematical relationship between the neighborhood of each sliding mode surface and the system uncertainty is derived. It is strictly proved...
In this study, we propose a novel fuzzy adaptive control technique for output tracking of uncertain nonlinear system, with experiment application to the APC system in roll mills. Firstly, the system is divided into two parts, one is known linear part and the other is unknown nonlinear part. Secondly, an adaptive fuzzy basic function is used only to identify the upper bound of the unknown part, which...
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