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Model Predictive Control of Diesel Engine Emissions Based on Neural Network Modeling
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.
Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control
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.
Benefits of Feedforward for Model Predictive Airpath Control of Diesel Engines
Two options for the feedforward are considered one based on a look-up table that specifies the feedforward as a function of engine speed and fuel injection rate, and another one based on a (non-rate-based) MPC that generates dynamic feedforward trajectories.
