An investigation of semiphysical artificial neural networks for multi-fuel combustion phasing control of spark ignition engines:

The number of engine control actuators and potential fuel sources are constantly increasing to meet fuel economy targets and global energy demand. The increased engine control complexity resulting from new actuators and fuels motivates the use of model-based control methodologies over map-based empirical approaches. Purely physics-based control techniques have the potential to decrease calibration burdens but must be complex to represent nonlinear engine behavior with low computational requirements. Artificial neural networks are recognized as powerful tools for modeling systems which exhibit nonlinear relationships, but they lack physical significance. Combining these two techniques to produce semiphysical artificial neural network models which provide acceptable accuracy while minimizing the artificial neural network size, the calibration effort and the computational intensity is the focus of this research. To minimize the size of the neural network, sensitivity analyses are carried out on the critical ...