Robust adaptive NN feedback linearization control of nonlinear systems

Abstract

In this paper, a robust adaptive neural network feedback linearization control law is presented for a class of nonlinear dynamic systems. First, the 'GL' matrices and the corresponding operator are introduced, which brings a new methodology into the analysis of neural networks. Secondly, the basic ideas of Feedback Linearization Control (FLC) of nonlinear systems are discussed. Finally, a robust adaptive neural network FLC of nonlinear systems is presented. It is shown that uniformly stable adaptation is assured and asymptotic tracking is achieved if Bounded Basis Functions (BBF) are used, and output tracking errors also converge to zero.