Title :
Application of Neural Network Dynamic Inversion Control Arithmetic
Author :
He Guang-Lin ; Wu Guo-hui ; Lao Li ; Chen Jing
Author_Institution :
Sch. of Aerosp. Sci. & Technol., Beijing Inst. of Technol., Beijing
Abstract :
The MAV is very little, so it is easily disturbed by the environment and has week stability. Due to its low Reynolds number, it is easy to be affected by the unstable air (turbulence and gusts), and other outside interference. Recent work in dynamic inversion with neural network may be applied to control a MAV where the reference commands include position, velocity, attitude and angular rate. This control technology can provide the MAV with an admirably command follow and steady control capability. Neural networks are used directly as the controller as well as indirect designs that are based on a neural network process model. The emulator is compiled by using neural network toolbox in MATLAB. Then the main result of this simulation is presented. It indicates that the method has high precision and strong stability, which can serve the control of MAV. Thereafter, an amendment about controlling system with dynamic inversion of the MAV is described.
Keywords :
aircraft control; control system synthesis; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; stability; MATLAB; MAV flight stability; Reynolds number; control technology; microair vehicle control; neural network dynamic inversion control arithmetic; neural network process model; neural network toolbox; nonlinear control system design; Aerodynamics; Aerospace control; Aerospace electronics; Aircraft; Arithmetic; Computer networks; Control systems; Mathematical model; Neural networks; Nonlinear dynamical systems; MATLAB; MAV; dynamic inversion; neural network;
Conference_Titel :
Electronic Computer Technology, 2009 International Conference on
Conference_Location :
Macau
Print_ISBN :
978-0-7695-3559-3
DOI :
10.1109/ICECT.2009.35
