Dynamic modeling and control system design based on neural network for a UAV with tricycle landing gear

Abstract

The neural network model based system identification with sliding mode learning algorithm is well modeling the system for tri-cycle landing gear plane with unknown coefficient. Sliding mode controller with reference model which is identified using neural network model is quite well control without any information of the system. Yaw rate based waypoint generator spaces the waypoint by rule. Path heading control simulation with commercial software is performed in real time neural network learning process. This algorithm is also tested in real vehicles, Soul(full-sized automobile) and F-16(scaled RC). The control performance and dynamic system identification are represented