Model Predictive Control as a Solution for Standardized Controller Synthesis and Reduced Development Time Application Example to Diesel Particulate Filter Temperature Control

Over the past few years, innovative engine layouts have enabled significant reductions in both fuel consumption and pollutant emissions. However, exponential growth of powertrain control strategies complexity has inevitably accompanied these achievements. As a result, control and calibration development time and effort have become an ever-growing concern in powertrain design. An illustrative example of this complexity is Diesel Particulate Filters (DPF), which requires periodic regeneration to eliminate the accumulated soot. The main challenge for a DPF is to enhance the efficiency of these regeneration events, which depend largely on the quality of the regeneration temperature control.

In this paper, we describe the DPF regeneration process, especially the main constraints and identification tests. We then give a simulation based comparison of two model based control solutions for the DPF thermal control during regeneration. Finally, we compare Renault's currently applied industrial gain scheduling controller with a prototype Model Predictive Control (MPC) designed by a software toolset called OnRAMP Design Suite, marketed by Honeywell. Specific attention is drawn to the comparison of the development times and effort.