IMC Based Iterative Learning Control of DOC Temperature During DPF Regerneration

Control of Diesel Oxidation Catalyst (DOC) outlet temperature is critical for the downstream Diesel Particulate Filter (DPF) regeneration. However, the complexities of the reactions in DOC make it difficult to manage its outlet temperature due to model uncertainties including time delay mismatch. DPF regeneration is treated as a batch process and the Internal Model Control (IMC) based Iterative Learning Control (ILC) was used for DOC outlet temperature control in this paper. The IMC-based ILC consists of the standard IMC and historical data based ILC. The standard IMC is based on the process model with time delay identified from the high fidelity DOC model in GT-Power. The ILC is designed based on historical information including the controller input, plant output, and model predictive output stored in the ‘memory’. Simulation results through high-fidelity GT-Power model are compared with IMC alone method and show that IMC based ILC can have fast and non-overshoot tracking after several iterations.