Modelling and Control of a Heavy-Duty Diesel Engine Gas Path with Gaussian Process Regression

This paper presents a method for modelling and controlling charge air pressure of a heavy-duty Diesel engine. The modelling method combines expert information about the system with a training dataset to provide a reasonable extrapolation behaviour as well as high accuracy. The dynamics of the gas path are modelled as a Wiener process with separate dynamics for each input. After identifying these dynamics, a controller based on input-output linearisation can be designed. The controller is shown to be able to track a set point of the gas path even after a sensor failure. To this end, the system behaviour is learned online to increase the accuracy. An evaluation against a reference model shows the performance of the method.