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Proceedings of the 2010 American Control Conference
This paper explores an adaptive feedback linearization for an uncertain nonlinear system using support vector regression (SVR). SVR, which assures global solution by quadratic programming (QP) problem, is used to learn the nominal dynamics of the input-output feedback-linearized system. Then, an adaptation algorithm of the offline-trained SVR is proposed for eliminating the offline-training error and uncertainties in the control process. In addition, the derivation of the adaptive ruledoi:10.1109/acc.2010.5530581 fatcat:hjshwvkgwbabdnlkq2uko6mh6m