Modelling and control of internal combustion engines using intelligent techniques

Lee, S.H., Howlett, R.J., Walters, S.D. and Crua, C. (2007) Modelling and control of internal combustion engines using intelligent techniques Cybernetics and systems, 38 (5-6). pp. 509-533. ISSN 0196-9722

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Abstract

This article will compare two different fuzzy-derived techniques for controlling small internal combustion engine and modeling fuel spray penetration in the cylinder of a diesel internal combustion engine. The first case study is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second case study used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is affected by a neural networks based on prior knowledge. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters. Future work is concentrating on the establishment of an improved neuro-fuzzy paradigm for adaptive, fast and accurate control of small internal combustion engines.

Item Type:Journal article
Subjects:G000 Computing and Mathematical Sciences > G600 Software Engineering
H000 Engineering > H300 Mechanical Engineering > H330 Automotive Engineering
G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
DOI (a stable link to the resource):10.1080/01969720701344293
Faculties:Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Engineering and Product Design Research > Automotive Engineering
ID Code:2188
Deposited By:Dr Cyril Crua
Deposited On:21 Jun 2007
Last Modified:31 Jan 2014 15:27

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