Open AccessHover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller
Author(s) -
Abdul-Basset Al- Hussein
Publication year - 2017
Publication title -
iraqi journal for electrical and electronic engineering/al-maǧallaẗ al-ʻirāqiyyaẗ al-handasaẗ al-kahrabāʼiyyaẗ wa-al-ilikttrūniyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2078-6069
pISSN - 1814-5892
DOI - 10.37917/ijeee.13.1.9
Subject(s) - control theory (sociology) , artificial neural network , benchmark (surveying) , adaptive control , controller (irrigation) , nonlinear system , computer science , lyapunov function , control engineering , reference model , control (management) , artificial intelligence , engineering , physics , software engineering , geodesy , quantum mechanics , agronomy , biology , geography
Unmanned aerial vehicles (UAV), have enormous important application in many fields. Quanser three degree of freedom (3-DOF) helicopter is a benchmark laboratory model for testing and validating the validity of various flight control algorithms. The elevation control of a 3-DOF helicopter is a complex task due to system nonlinearity, uncertainty and strong coupling dynamical model. In this paper, an RBF neural network model reference adaptive controller has been used, employing the grate approximation capability of the neural network to match the unknown and nonlinearity in order to build a strong MRAC adaptive control algorithm. The control law and stable neural network updating law are determined using Lyapunov theory.