Simulation of nonlinear identification and control of unmanned aerial vehicle: an artificial neural network approach

Authors:
Bhaskar Prasad Rimal;Hyoim Shin;Eunmi Choi
Affiliations:
Graduate School of Business IT, Kookmin University, Seoul, Korea;Graduate School of Business IT, Kookmin University, Seoul, Korea;Graduate School of Business IT, Kookmin University, Seoul, Korea
Venue:
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Year:
2009

Citing 3
Cited 0

The basic ideas in neural networks

Communications of the ACM
Control of nonlinear dynamical systems using neural networks. II. Observability, identification, and control

IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial Neural Networks (ANNs) are widely applied nowadays for classification, identification, control, diagnostics, recognition, etc. They can be implemented for identification of dynamic systems. The concept of ANN is highly used in design and simulation of control system of Unmanned Aerial Vehicles (UAVs). Controller design for UAV is subject to time varying and non-linear model parameters. The objective of this work is to simulate the nonlinear identification of a dynamic system which is based on its response to standard signals. The non linear identification of the state space methods is based on model reference control. For model reference control, the controller is a neural network that is trained to control a plant so that it follows a reference model. The neural network plant model is used to assist in the controller training. In this paper we simulate the modeling capabilities of a state space neural network, to act as an observer for a non-linear process allowing a simultaneous estimation of parameters and states.