Observer-Based Adaptive Neural Control for a Class of Nonlinear Non-affine Systems with Unknown Gain Sign

This paper presents an adaptive neural network output feedback controller for a class of uncertain SISO nonlinear non-affine systems. Since the system states are not required to be available for measurement, an observer is designed to estimate the system states. Comparing to existing results, this method does not require a priori knowledge about the sign of control gain direction. To deal with the unknown sign of the control gain, the Nussbaum-type function is used. By using neural network, the unknown nonlinear function is approximated and a robustifying term is used to reduce the approximation error and compensate the effect of external disturbance. The stability of the closed-loop system is analyzed by using Lyapunov method. Theoretical results are illustrated through simulation example. Numerical simulations confirm the effectiveness of the proposed method.