Improved scheme of membership function optimisation for fuzzy air-fuel ratio control of GDI engines

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air-fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional-integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.