Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
Volume 224, Issue D1, Pages 55-72Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1243/09544070JAUTO1196
Keywords
idle-speed control; least-squares support vector machines; control optimization; genetic algorithm; particle swarm optimization
Funding
- University of Macau [ULO11/09-Y1/EME/WPK01/FST]
- Science and Technology Development Fund of Macau [019/2007/A]
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This paper proposes a novel modelling and optimization approach for steady state and transient performance tune-up of an engine at idle speed. In terms of modelling, Latin hypercube sampling and multiple-input and multiple-output (MIMO) least-squares support vector machines (LS-SVMs) are proposed to build an engine idle-speed model based on experimental sample data. Then, a genetic algorithm (GA) and particle swarm optimization (PSO) are applied to obtain an optimal electronic control unit setting automatically, under various user-defined constraints. All of the above techniques mentioned are artificial intelligence techniques. To illustrate the advantages of the MIMO LS-SVM, a traditional multilayer feedforward neural network (MFN) is also applied to build the engine idle-speed model. The modelling accuracies of the MIMO LS-SVM and MFN are also compared. This study shows that the predicted results using the estimated model from the LS-SVM are in good agreement with the actual test results. Moreover, both the GA and PSO optimization results show an impressive improvement on idle-speed performance in a test engine. The optimization results also indicate that PSO is more efficient than the GA in an idle-speed control optimization problem based on the LS-SVM model. As the proposed methodology is generic, it can be applied to different engine modelling and control optimization problems.
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