Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift

This paper addresses the changes in engine emissions due to in-use component changes through the synergistic application of predictive control, machine learning, and onboard adaptation. In particular, we consider an adaptive economic Model Predictive Control (eMPC) strategy to mitigate the effects of performance drift on Nitrogen Oxides (NOx) and Soot emissions from compression ignition (diesel) engines. A performance drift block, which applies a multiplier and offset to nominal emissions, is integrated with a high-fidelity Neural Network (NN) plant model to simulate these characteristic changes. To counteract variability, two online adaptation methods are integrated within the eMPC framework: One is based on Recursive Least Squares (RLS) and another on a continuously updated online NN. The proposed control architecture is validated through simulations over standard transient cycles. Results demonstrate that while the rate-based eMPC possesses inherent robustness to performance drift, in particular, for the formulation of eMPC that involves NOx penalty in the cost function, online adaptation further facilitates satisfying emission constraints. In particular, both adaptive methods improve Soot limit enforcement compared to a non-adaptive controller, with the online NN providing superior performance by capturing the nonlinear dynamics of in-use changes.