Analysis of semi-physical neural network structure for model-based ignition timing control of multi-fuel SI engines Online publication date: Thu, 26-Jan-2017
by Baitao Xiao; Shu Wang; Robert Prucka
International Journal of Powertrains (IJPT), Vol. 5, No. 4, 2016
Abstract: This research investigates the use of a semi-physical neural network for combustion phasing control of a multi-fuel spark ignition engine. The influence of model structure and training data set composition are each analysed for their influence on combustion phasing control accuracy under steady-state and transient engine operating conditions. The model structure developed for this research utilises both fuel sensitive and combustion related physical models as inputs, with an aim to minimise neural network size and increase its generalisation capability. Optimisation of the network structure is also studied to evaluate overall robustness and ensure control stability. Real-time engine test results show satisfactory combustion phasing control performance with multiple fuels for both steady state and transient conditions. Moreover, demonstration of the differences of controller performance brought by the steady state and transient training data samples will be also provided.
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