Neural networks for real-time nonlinear control of a variable geometry turbocharged diesel engine

New engines are submitted to emission standards that are becoming more and more restrictive. Diesel engines are typically equipped with variable geometry turbo-compressor, exhaust gas recirculation system, high-pressure common rail system and post-treatment devices in order to meet these legislative requirements. Consequently, the control of diesel engines becomes a very difficult task involving five to 10 control variables that interact with each other and that are highly nonlinear. Until the present day, the control schemes integrated in the engine's controller are all based on static maps identified by steady-state engine mapping. Afterward, these schemes are adjusted and calibrated in the vehicle using various control techniques in order to assure a better dynamic response of the engine under dynamic load. In this paper, we are interested in developing a mathematical optimization process that searches for the optimal control scheme under static and dynamic operating conditions. Firstly, we suggest modeling the engine and its emissions using mean value models which require limited experiments and are in good agreement with the experimental data. These models are then used in a dynamic optimization process based on the Broyden-Fletcher-Goldfarb-Shanno algorithm in order to find the optimal control scheme of the engine. The results show a reduction of the engine emissions without deteriorating its performance. Finally, we propose a practical control technique based on neural networks in order to apply these control schemes online to the engine. The results are promising. Copyright (C) 2007 John Wiley & Sons, Ltd.