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Gaussian networks for fuel injection control
- Source :
- Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 215:1053-1068
- Publication Year :
- 2001
- Publisher :
- SAGE Publications, 2001.
-
Abstract
- This paper proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past, neural controllers for this problem have centred on using a cerebellar model articulation controller (CMAC) type network with some success. The current production engine control units also use look-up tables in their fuel injection controllers, and if adaptation is permitted to these look-up tables the overall effect closely mimics the CMAC network. Here it is shown that an RBF network with significantly fewer nodes than a CMAC network is capable of delivering superior control performance on a mean value engine model simulation. The proposed approach requires no a priori knowledge of the engine systems, and on-line learning is achieved using gradient descent updates. The RBF network is then implemented on a four-cylinder engine and, after a minor modification, outperforms a production engine control unit.
Details
- ISSN :
- 20412991 and 09544070
- Volume :
- 215
- Database :
- OpenAIRE
- Journal :
- Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
- Accession number :
- edsair.doi...........5b8e387cc9adb916fd390d4d55b031b3