Hierarchical Predictive Control of a Combined Engine/Selective Catalytic Reduction System with Limited Model Knowledge

In this article, the problem of minimizing the overall operational cost of a heavy-duty off-highway diesel engine combined with a selective catalytic reduction (SCR) catalyst is considered. Here, we propose a hierarchical model-based scheme described as an optimal control problem. The sequence of resulting optimal control values are setpoints for the underlying engine controller, applied in a model predictive control (MPC) fashion. The presented scheme meets several demands. While minimizing the overall costs, it handles box constraints for the control variables as well as a nonlinear NO_x-conversion rate constraint ensuring that a given emission target is met. The approach makes use of Gaussian process models for the input-output behavior of the underlying components and a technique for online adaptation. Thus, the presented hierarchical scheme is able to compensate model uncertainties and aging effects of engine, air path, and SCR catalyst. Moreover, in comparison to the literature, our approach doesn’t require detailed models of the underlying components, and the hierarchical, modular design allows the applicability to different engines and SCR controllers. We illustrate the proposed approach by several simulation results.