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A new design approach for an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching(More)
A novel adaptive fuzzy-neural sliding-mode controller with H(infinity) tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary(More)
A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode(More)
In this paper, an adaptive fuzzy controller for strict-feedback canonical nonlinear systems is proposed. The completely unknown nonlinearities and disturbances of the systems are considered. Since fuzzy logic systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory is employed to derive the(More)
In this study, the robust least square support vector machines for regression (RLS-SVMR) is proposed to deal with training data set with outliers. There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector regression (SVR) approach is used to filter out the outliers in the training data set. Due(More)
In this study, the hybrid support vector machines for regression (HSVMR) is proposed to deal with training data set with outliers for fuzzy neural networks (FNNs). There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the(More)
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