System Identification of UAV Alap-alap using back propagation neural network

Afrias Sarotama, Benyamin Kusumo Putro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A good model is necessary in order to design a controller of a system off-line. It is especially beneficial in the implementation of new advanced control schemes in Unmanned Aerial Vehicle (UAV). Considering the safety and benefit of an off-line tuning of the UAV controllers, this paper identifies a dynamic MIMO UAV nonlinear system which is derived based on the collection of input-output data taken from a test flights (36250 samples data). These input-output sample flight data are grouped into two flight data sets. The first flight data set, a chirp signal, is used for training the neural network in order to determine parameters (weights) for the network. Validation of the network is performed using the second data set, which is not used for training, and is a representation of UAV circular flight movement. An artificial neural network is trained using the training data set and thereafter the network is excited by the second set input data set. The predicted outputs based on our proposed Neural Network model is similar to the desired outputs (roll, pitch and yaw) which has been produced by real UAV system.

Original languageEnglish
Title of host publicationMechatronics, Robotics and Automation
Pages1212-1219
Number of pages8
DOIs
Publication statusPublished - 22 Oct 2013
Event2013 International Conference on Mechatronics, Robotics and Automation, ICMRA 2013 - Guangzhou, China
Duration: 13 Jun 201314 Jun 2013

Publication series

NameApplied Mechanics and Materials
Volume373-375
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2013 International Conference on Mechatronics, Robotics and Automation, ICMRA 2013
CountryChina
CityGuangzhou
Period13/06/1314/06/13

Keywords

  • Artificial neural network identification
  • Back propagation
  • UAV

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