Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution

The need for a high fidelity model for design,analysis and implementation of an unmanned helicopter system(UHS) in various emerging civil applications cannot be underestimated. However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is note...

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Main Authors:	Tijani, Ismaila B., Akmeliawati, Rini, Legowo, Ari, Budiyono, Agus
Format:	Article
Language:	English
Published:	Elsevier Science Ltd. 2014
Subjects:	TJ212 Control engineering
Online Access:	http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/1/eaai_tijani2014.pdf

id	iium-41736
recordtype	eprints
spelling	iium-417362015-03-26T03:13:02Z http://irep.iium.edu.my/41736/ Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution Tijani, Ismaila B. Akmeliawati, Rini Legowo, Ari Budiyono, Agus TJ212 Control engineering The need for a high fidelity model for design,analysis and implementation of an unmanned helicopter system(UHS) in various emerging civil applications cannot be underestimated. However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is noted to be highly challenging due to the complex nonlinear characteristics of the helicopter system.On the other hand, the problem of determining network architecture for optimal/sub-optimal performances has been one of the major challenges in the use of the non parametric approach based on Nonlinear AutoRegressive with eXogenous inputs Network(NARX-network). The performance of the NARX network in terms of complexity and accuracy is largely dependent on the network architecture. The current approach in the literature has been largely based on trial and error, while most of the reported optimization approaches have limited the domain of the problem to a single objective problem. This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. The performance of the proposed optimized model is benchmarked with one of the previously reported architectures for a similar system. The optimized model outperformed the previous model architecture with up to 55% performance improvement. Apart from the effectiveness of the optimized model, the proposed design algorithm is expected to facilitate timely development of the nonparametric model of the helicopter system. Elsevier Science Ltd. 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/41736/1/eaai_tijani2014.pdf Tijani, Ismaila B. and Akmeliawati, Rini and Legowo, Ari and Budiyono, Agus (2014) Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution. Engineering Applications of Artificial Intelligence, 33. pp. 99-115. ISSN 0952-1976 http://dx.doi.org/10.1016/j.engappai.2014.04.003 doi:10.1016/j.engappai.2014.04.003
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TJ212 Control engineering
spellingShingle	TJ212 Control engineering Tijani, Ismaila B. Akmeliawati, Rini Legowo, Ari Budiyono, Agus Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
description	The need for a high fidelity model for design,analysis and implementation of an unmanned helicopter system(UHS) in various emerging civil applications cannot be underestimated. However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is noted to be highly challenging due to the complex nonlinear characteristics of the helicopter system.On the other hand, the problem of determining network architecture for optimal/sub-optimal performances has been one of the major challenges in the use of the non parametric approach based on Nonlinear AutoRegressive with eXogenous inputs Network(NARX-network). The performance of the NARX network in terms of complexity and accuracy is largely dependent on the network architecture. The current approach in the literature has been largely based on trial and error, while most of the reported optimization approaches have limited the domain of the problem to a single objective problem. This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. The performance of the proposed optimized model is benchmarked with one of the previously reported architectures for a similar system. The optimized model outperformed the previous model architecture with up to 55% performance improvement. Apart from the effectiveness of the optimized model, the proposed design algorithm is expected to facilitate timely development of the nonparametric model of the helicopter system.
format	Article
author	Tijani, Ismaila B. Akmeliawati, Rini Legowo, Ari Budiyono, Agus
author_facet	Tijani, Ismaila B. Akmeliawati, Rini Legowo, Ari Budiyono, Agus
author_sort	Tijani, Ismaila B.
title	Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
title_short	Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
title_full	Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
title_fullStr	Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
title_full_unstemmed	Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
title_sort	nonlinear identification of a small scale unmanned helicopter using optimized narx network with multiobjective differential evolution
publisher	Elsevier Science Ltd.
publishDate	2014
url	http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/ http://irep.iium.edu.my/41736/1/eaai_tijani2014.pdf
first_indexed	2023-09-18T20:59:41Z
last_indexed	2023-09-18T20:59:41Z
_version_	1777410539381587968

Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution

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