Neural network-based air handling control for modern diesel engines

Abstract

Complex air handling systems, featuring technologies such as exhaust gas recirculation (EGR) and variable geometry turbochargers (VGTs), are commonly used in modern diesel engines to meet stringent emissions and fuel economy requirements. The control of diesel air handling systems with EGR and VGTs is challenging because of their nonlinearity and coupled dynamics. In this paper, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are applied to control the low pressure (LP) EGR valve position and VGT vane position simultaneously on a light-duty multi-cylinder diesel engine. Intake manifold pressure (IMP) and air-fuel equivalence ratio, or [Formula: see text], are selected as the control objectives since they directly impact engine emissions and cylinder power output. Meanwhile both signals are available on production engines, so no additional hardware costs for measurement systems will be introduced. Both transient and steady state experimental data are separately used to train the two categories of neural networks (NNs). The NNs with minimum mean square error (MSE) for the training data sets are compared to conventional proportional-integral (PI) control and model predictive control (MPC). The most accurate NN controller has almost no overshoot during the transient process while the steady state error [Formula: see text] and IMP are at most 7.0% and 3.8%, respectively, under a wide range of engine speeds from 2500 to 4000 rpm, thus showing the potential of this approach.

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

“…Due to the strong capability for system dynamic approximation, ANN is the most frequent-used method in diesel engine data-driven control as well. [122][123][124] Generally, the ANN model is adopted to describe the dynamics in diesel engines, and then the strategies of control or optimization are developed based on ANN data-driven model. Yang et al 118 designed an efficient approach of prediction and optimization based on ANN for waste heat recovery in diesel engines.…”

“…At last, the fuzzy logic controller was implemented successfully. 79 Niu et al, 80 Taghavifar et al, 81 Roy et al, 82 Mohamed Ismail et al, 100 Ghobadian et al, 97 Piloto-Rodrı ´guez et al 98 Porteiro et al, 106 Liu et al 107 Yang et al, 118 Omran et al, 119 Etghani et al, 120 Tian et al, 121 Peng et al, 122 Li et al, 123 Ke et al 124…”