Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

TL;DR

This paper integrates machine learning with model predictive control to optimize engine performance, reducing emissions and fuel consumption efficiently by mimicking complex controllers with significantly less computational effort.

Contribution

It introduces a deep learning-based imitation of MPC for engine control, achieving near-optimal emission reduction with substantially lower computational cost.

Findings

Imitative controller reduces NOx emissions effectively.

50 times less computation required compared to online MPC.

Performance comparable to online MPC in emission reduction.

Abstract

The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of an Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the \nox~emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC compared to the Cummins production controller. In addition, the imitative controller requires 50 times less computation time compared to that of the online MPC optimization.