Hybrid Air/Fuel Ratio Control Using the Adaptive Estimation and Neural Network

The paper describes a hybrid air-fuel mixture control system that uses neural network and the direct adaptive algorithm. The A/F ratio stabilization to the stoichiometric value is required to obtain maximum efficiency of the three-way catalytic converter operation. The issues of the algorithm synthesis of the adaptive control of the fuel injection have been formulated. This was supplemented by the presentation of the state-of-the-art in the adaptive control theory as applied to non-stationary random object identification.

The control algorithms of the fuel injection have been reviewed and classified. The fuel injection algorithms in the SI engine have been described and differentiated in terms of the used engine model and regulator structure. The algorithms comprise elements of the object modeling as well as adaptive coefficients for the control quality of the air-fuel ratio in the steady and non-steady conditions. The direct adaptation requires defining an adaptive coefficient, which is a compromise between operation speed and estimation accuracy.

Our algorithm is connected to heuristic procedures selecting the learning speed factor in adaptive estimator, and the most reliable estimator depending on the engine working conditions. Mechanisms based on the mathematics of artificial neural networks have been used for that purpose. The aim of the expert system is to determine the adaptive coefficient. In this paper an idea of using artificial neural network to determine the adaptive coefficient is presented.

The paper includes results from the computer simulation of a regulation of the A/F ratio quality. The data needed for the estimation (the engine model as non-stationary random object) was obtained from the road tests. The paper includes estimation results and conclusions.