MODEL REFERENCE ADAPTIVE CONTROL USING NEURAL NETWORKS

Abstract

The thesis presents a quick overview of Model Reference Adaptive Control (MRAC). The thesis presents model reference based neural network structure that can be used for adaptive control of linear and nonlinear processes. The proposed MRAC neural network scheme is simulated to control the movement of a simple, single-link robot arm. The simulation results show that neural network based MRAC can give satisfactory performance requirements