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This paper presents the results of developing a multi-layer Neural Network(NN) to represent diesel engine emissions and integrating this NN into controldesign. Firstly, a NN is trained and validated to simultaneously predict oxidesof nitrogen (N Ox) and Soot using both transient and steady-state data. Basedon the input-output correlation analysis, inputs to NN with the highestinfluence on the emissions are selected while keeping the NN structure simple.Secondly, a co-simulation framework is implemented to integrate the NNemissions model with a model of a diesel engine airpath system built inGT-Power and used to identify a low-order linear parameter-varying (LPV) modelfor emissions prediction. Finally, an economic supervisory model predictivecontroller (MPC) is developed using the LPV emissions model to adjust setpointsto an inner-loop airpath tracking MPC. Simulation results are reportedillustrating the capability of the resulting controller to reduce N Ox, meetthe target Soot limit, and track the adjusted intake manifold pressure andexhaust gas recirculation (EGR) rate targets.

Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control