  This paper addresses the control of diesel engine nitrogen oxides (NOx) and
Soot emissions through the application of Model Predictive Control (MPC). The
developments described in the paper are based on a high-fidelity model of the
engine airpath and torque response in GT-Power, which is extended with a
feedforward neural network (FNN)-based model of engine out (feedgas) emissions
identified from experimental engine data to enable the controller co-simulation
and performance verification. A Recurrent Neural Network (RNN) is then
identified for use as a prediction model in the implementation of a nonlinear
economic MPC that adjusts intake manifold pressure and EGR rate set-points to
the inner loop airpath controller as well as the engine fueling rate. Based on
GT-Power engine model and FNN emissions model, the closed-loop simulations of
the control system and the plant model, over different driving cycles,
demonstrate the capability to shape engine out emissions response by adjusting
weights and constraints in economic MPC formulation.
