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...
| Main Authors: | , , , , |
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| Format: | Book Section |
| Language: | English |
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Institute of Electrical and Electronics Engineering (IEEE)
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/9606/ http://eprints.utm.my/9606/1/KamarulBaharinTawi2007_AdaptiveNeuralNetworkOptimisationControl.pdf |
| _version_ | 1848891892183334912 |
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| 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 |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T21:05:11Z |
| format | Book Section |
| id | utm-9606 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:05:11Z |
| publishDate | 2007 |
| publisher | Institute of Electrical and Electronics Engineering (IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-96062017-09-03T09:54:29Z http://eprints.utm.my/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/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 |
| 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 |
| title | 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_short | 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 |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/9606/ http://eprints.utm.my/9606/ http://eprints.utm.my/9606/ http://eprints.utm.my/9606/1/KamarulBaharinTawi2007_AdaptiveNeuralNetworkOptimisationControl.pdf |