2018 Volume 49 Issue 6 Pages 1162-1166
This paper considers machine learning based virtual design process of engine control system, and the demonstration in a diesel engine air path control is shown. This process contains two steps of machine learning. In the first step, a control-oriented forward model that predicts the transient behavior of the engine is learned from detailed engine model by using recurrent neural network (RNN). In the second step, an inverse model that determines the optimal control inputs to follow the references is learned from the numerical computation results of the offline model predictive control (MPC). The forward and inverse models could be used as a state observer and a controller, respectively, in a control system. An experiment of a diesel air path control system designed by the process was conducted using rapid control prototyping (RCP), and its following capability to the reference was demonstrated.
