Many control strategies has been made for compensating the uncertainties of nonlinear systems where uncertain parameters are assumed to be constant or have slow variations. Some uncertainties can be caused by time-varying parameters or disturbances that entered the system. By considering the fact that conventional adaptive strategies have restrictions in dealing with time-varying uncertainties, investigation of an adaptive control for time-varying systems is a challenging problem to the control community. This study presents an adaptive neural control scheme for a justify; LINE-HEIGHT: normal; MARGIN: 0cm 0cm 0pt; mso-outline-level: 1" nonlinear non- affine system with time-varying uncertainties is studied. The existence of a controller is proved that the stability of the closed loop system has been ensured considering some theorems. Then a RBF neural network is used to design this ideal controller. Also, a robust term is applied for compensating RBF neural network error approximation. Proposed scheme ensured stability of the closed loop and tracking error converged to zero. For the last step, we use WNN with adjustable parameters as a approximator of uncertainties. In addition to updating weights using periodic laws, scale and shift parameters are updated by integral laws. Desired performances achieve using WNN as an approximator with less neurons than RBF neural network. Some simulation results are provided to illustrate the efficiency of proposed control scheme in this study in every section. Keywords — Time-varying uncertainties, Adaptive Neural Control, Nonlinear affine systems, Radial Basic Function(RBF), Periodic adaptation , Nonlinear non-affine systems, Wavelet Neural Network(WNN).
