Adaptive Internal Model Based Control of the RGF Using Online Map Learning and Statistical Feedback Law

Residual gas fraction (RGF) defined as the ratio of residual gas mass to the mass of total gas burned in one cylinder is an important variable for managing combustion quality and combustion mode. This article proposes an adaptive internal model control (AIMC) based framework that synthesizes on-board map calibration and statistical feedback criterion to track a desired RGF target. The model structure of plant dynamics (or its inverse) in AIMC is derived as a cascade combination of a steady mapping and a parameter-varying variable valve timing (VVT) dynamics (or inverse). The on-board map calibration algorithm is employed to adapt the operating-point-dependent steady mapping since the system behavior may change. Then, a VVT compensatory controller in the AIMC was designed based on the online identification of the parameter-varying VVT dynamics model and its inverse. Moreover, a statistical criterion, namely, the hypothesis test was creatively introduced in the feedback channel of the AIMC to filter out the stochastic noise of the RGF. The experiment results show that the AIMC-based framework significantly improves the RGF tracking performance in both steady and transient states.