  Machine learning (ML) and a nonlinear model predictive controller (NMPC) are
used in this paper to minimize the emissions and fuel consumption of a
compression ignition engine. In this work machine learning is applied in two
methods. In the first application, ML is used to identify a model for
implementation in model predictive control optimization problems. In the second
application, ML is used as a replacement of the NMPC where the ML controller
learns the optimal control action by imitating or mimicking the behavior of the
model predictive controller. In this study, a deep recurrent neural network
including long-short term memory (LSTM) layers are used to model the emissions
and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine.
This model is then used for model predictive controller implementation. Then, a
deep learning scheme is deployed to clone the behavior of the developed
controller. In the LSTM integration, a novel scheme is used by augmenting
hidden and cell states of the network in an NMPC optimization problem. The
developed LSTM-NMPC and the imitative NMPC are compared with the Cummins
calibrated Engine Control Unit (ECU) model in an experimentally validated
engine simulation platform. Results show a significant reduction in Nitrogen
Oxides (\nox) emissions and a slight decrease in the injected fuel quantity
while maintaining the same load. In addition, the imitative NMPC has a similar
performance as the NMPC but with a two orders of magnitude reduction of the
computation time.
