Nonlinear MPC of a Heavy-Duty Diesel Engine With Learning Gaussian Process Regression
D. Bergmann, Karsten Harder, Jens Niemeyer, Knut Graichen
Abstract
This contribution presents a method for modeling and controlling a heavy-duty biturbocharged diesel engine. The modeling scheme can incorporate expert knowledge of the control relevant combustion quantities into Gaussian process models. A nonlinear model predictive controller (MPC) is used to control the engine outputs subject to the gas path dynamics and nonlinear constraints for the emissions and for the sake of engine protection. In addition, an online learning scheme based on Gaussian process regression is used to compensate for model uncertainties due to aging effects and manufacturing tolerances. A consistent model smoothing strategy is derived to preserve the given expert knowledge and to avoid abrupt reactions of the MPC due to the online learning of the models. All parts of the controller are implemented with respect to real-time feasibility and small memory footprint. Experimental results for a real-world heavy-duty engine demonstrate the performance and the online learning ability of the presented nonlinear MPC scheme that may be transferred to various diesel engine applications.