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Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission

Continuously variable transmissions (CVT) have received great interest as viable alternative to discrete ratio transmission in passenger vehicle. It is generally accepted that CVTs have the potential to provide such desirable attributes as: a wider range ratio, good fuel economy, shifting ratio cont...

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Main Authors: Tawi, Kamarul Baharin, Ariyono, Sugeng, Jamaluddin, Hishamuddin, Hussein, Mohamed, Supriyo, Bambang
Format: Book Section
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2007
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/9606/1/KamarulBaharinTawi2007_AdaptiveNeuralNetworkOptimisationControl.pdf
http://eprints.utm.my/id/eprint/9606/
http://dx.doi.org/10.1109/ICIAS.2007.4658386
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spelling my.utm.96062017-09-03T09:54:29Z http://eprints.utm.my/id/eprint/9606/ Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission Tawi, Kamarul Baharin Ariyono, Sugeng Jamaluddin, Hishamuddin Hussein, Mohamed Supriyo, Bambang TJ Mechanical engineering and machinery Continuously variable transmissions (CVT) have received great interest as viable alternative to discrete ratio transmission in passenger vehicle. It is generally accepted that CVTs have the potential to provide such desirable attributes as: a wider range ratio, good fuel economy, shifting ratio continuously and smoothly and good driveability. With the introduction of continuously variable transmission (CVT), maintaining constant engine speed based on either its optimum control line or maximum engine power characteristic could be made possible. This paper describes the simulation work in drivetrain area carried out by the Drivetrain Research Group (DRG) at the Automotive Development Centre (ADC), Universiti Teknologi Malaysia, Skudai Johor. The drivetrain model is highly non-linear; and it could not be controlled satisfactorily by common linear control strategy such as PID controller. To overcome the problem, the use of adaptive neural network optimisation control (ANNOC) is employed to indirectly control the engine speed by adjusting pulley CVT ratio. In this work, the simulation results of ANNOC into drivetrain model showed that this highly non-linear behaviour could be controlled satisfactorily. Institute of Electrical and Electronics Engineering (IEEE) 2007-11 Book Section PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/9606/1/KamarulBaharinTawi2007_AdaptiveNeuralNetworkOptimisationControl.pdf Tawi, Kamarul Baharin and Ariyono, Sugeng and Jamaluddin, Hishamuddin and Hussein, Mohamed and Supriyo, Bambang (2007) Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission. In: International Conference on Intelligent and Advanced Systems 2007. Institute of Electrical and Electronics Engineering (IEEE), pp. 257-262. ISBN 978-1-4244-1355-3 http://dx.doi.org/10.1109/ICIAS.2007.4658386 doi : 10.1109/ICIAS.2007.4658386
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Tawi, Kamarul Baharin
Ariyono, Sugeng
Jamaluddin, Hishamuddin
Hussein, Mohamed
Supriyo, Bambang
Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
description Continuously variable transmissions (CVT) have received great interest as viable alternative to discrete ratio transmission in passenger vehicle. It is generally accepted that CVTs have the potential to provide such desirable attributes as: a wider range ratio, good fuel economy, shifting ratio continuously and smoothly and good driveability. With the introduction of continuously variable transmission (CVT), maintaining constant engine speed based on either its optimum control line or maximum engine power characteristic could be made possible. This paper describes the simulation work in drivetrain area carried out by the Drivetrain Research Group (DRG) at the Automotive Development Centre (ADC), Universiti Teknologi Malaysia, Skudai Johor. The drivetrain model is highly non-linear; and it could not be controlled satisfactorily by common linear control strategy such as PID controller. To overcome the problem, the use of adaptive neural network optimisation control (ANNOC) is employed to indirectly control the engine speed by adjusting pulley CVT ratio. In this work, the simulation results of ANNOC into drivetrain model showed that this highly non-linear behaviour could be controlled satisfactorily.
format Book Section
author Tawi, Kamarul Baharin
Ariyono, Sugeng
Jamaluddin, Hishamuddin
Hussein, Mohamed
Supriyo, Bambang
author_facet Tawi, Kamarul Baharin
Ariyono, Sugeng
Jamaluddin, Hishamuddin
Hussein, Mohamed
Supriyo, Bambang
author_sort Tawi, Kamarul Baharin
title Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
title_short Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
title_full Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
title_fullStr Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
title_full_unstemmed Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
title_sort adaptive neural network optimisation control of ice for vehicle with continuously variable transmission
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2007
url http://eprints.utm.my/id/eprint/9606/1/KamarulBaharinTawi2007_AdaptiveNeuralNetworkOptimisationControl.pdf
http://eprints.utm.my/id/eprint/9606/
http://dx.doi.org/10.1109/ICIAS.2007.4658386
_version_ 1643645203625541632
score 13.251813

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