DocumentCode :
3201993
Title :
Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission
Author :
Ariyono, Sugeng ; Tawi, Kamarul Baharin ; Jamaluddin, Hishamuddin ; Hussein, Mohamed ; Supriyo, Bambang
Author_Institution :
Dept. of Mech. Eng., Politeknik Negeri Semarang, Semarang
fYear :
2007
fDate :
25-28 Nov. 2007
Firstpage :
257
Lastpage :
262
Abstract :
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.
Keywords :
adaptive control; angular velocity control; automobiles; neurocontrollers; nonlinear control systems; optimisation; three-term control; Automotive Development Centre; Drivetrain Research Group; ICE; Skudai Johor; Universiti Teknologi Malaysia; adaptive neural network optimisation control; continuously variable transmission; discrete ratio transmission; drivetrain model; engine speed; fuel economy; linear control strategy; maximum engine power characteristic; Adaptive control; Adaptive systems; Automotive engineering; Engines; Fuel economy; Ice; Mechanical power transmission; Neural networks; Programmable control; Vehicles; Adaptive neural network; CVT control; electromechanical CVT; engine speed control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4244-1355-3
Electronic_ISBN :
978-1-4244-1356-0
Type :
conf
DOI :
10.1109/ICIAS.2007.4658386
Filename :
4658386
Link To Document :
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