Neuro-Fuzzy Network Based Adaptive Tracking Controller for a Nonlinear System


In this paper, a neuro-fuzzy network-based adaptivetracking controller is suggested for controlling a type ofnonlinear system. Where two neuro-fuzzy networks have beenused to learn the system dynamics uncertainty bounds by usingLyapunov method. Then the output of these two networks isused to build a sliding mode controller. The stability of thecontrol system is proved and stable neuro-fuzzy controllerparameters adjustment laws are selected using Lyapunovtheory. The simulation case study shows that the controlled systemtracking the reference model effectively with smooth controleffort and robust performance has been achieved