Adaptive Control of Nonaffine Nonlinear Systems by Neural state Feedback

Abstract

In this paper, a new control method for a class of single input single output nonaffine nonlinear systems is considered using radial basis function (RBF) neural networks (NNs). Firstly, the existence of an ideal implicit feedback linearization control is established based on implicit function theory. An online RBF system is introduced to approximate this ideal implicit feedback linearization law. The proposed neural fuzzy adaptive controller ensures that the system output tracks a given bounded reference signal, while the closed loop stability results are provided and guaranteed using Lyapunov theory. The effectiveness of the proposed controller is illustrated through a simulation to a nonaffine nonlinear system.