Adaptive feedback linearization for an uncertain nonlinear system using support vector regression

Jongho Shin, H Jin Kim, Youdan Kim
2010 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 rule
more » ... ptive rule considers the controller singularity problem by utilizing the affine property of the nonlinear system and the concept of the virtual control. Uniformly ultimately bound property of the overall system is analyzed by the Lyapunov stability theory. Simulations using a longitudinal dynamics of the F-16 model validate the performance of the proposed approach.
doi:10.1109/acc.2010.5530581 fatcat:hjshwvkgwbabdnlkq2uko6mh6m