This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of nitrogen (N Ox) and Soot using both transient and steady-state data. Based on the input-output correlation analysis, inputs to NN with the highest influence on the emissions are selected while keeping the NN structure simple. Secondly, a co-simulation framework is implemented to integrate the NN emissions model with a model of a diesel engine airpath system built in GT-Power and used to identify a low-order linear parameter-varying (LPV) model for emissions prediction. Finally, an economic supervisory model predictive controller (MPC) is developed using the LPV emissions model to adjust setpoints to an inner-loop airpath tracking MPC. Simulation results are reported illustrating the capability of the resulting controller to reduce N Ox, meet the target Soot limit, and track the adjusted intake manifold pressure and exhaust gas recirculation (EGR) rate targets.
@article{arxiv.2311.03552,
title = {Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control},
author = {Jiadi Zhang and Xiao Li and Mohammad Reza Amini and Ilya Kolmanovsky and Munechika Tsutsumi and Hayato Nakada},
journal= {arXiv preprint arXiv:2311.03552},
year = {2023}
}