Neuro-Fuzzy Network Based Adaptive Tracking Controller for a Nonlinear System
Abstract
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
Keywords
Neuro-Fuzzy network, Adaptive Control, Model Reference Control, Uncertain Dynamics, sliding mode controllerMetrics