Local Linear Models adaptation for a 4 Inj - 2PP common-rail pressure system

The implementation of a Neuro-Fuzzy nonlinear adaptive structure, with Local Linear Models (LLM), designed for fuel pressure estimation in diesel common-rail (CR) hydraulic system, represents the main topic. Hydraulic systems, in general, are nonlinear and engineers have often struggled to find the best solution to approximate the input-output dependencies. Powerful tools are necessary for splitting the input space in smaller pieces where linear approximations can be considered satisfactory. Neuro-Fuzzy networks, combined with LLM represent the best solution in this case. Using appropriate numerical models, these architectures can be implemented in a real-time environment designed for on-line adaptation of the linear models parameters. The paper demonstrates that the LLMs, and hence, the whole dynamic models parameters of the CR’s NeuroFuzzy developed architecture, can be adapted in an on-line environment. The practical results are favorable. Keywords—local linear; adaptive; Neuro-Fuzzy; commonrail; pressure; system model