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Abstract

This study presents an airflow correction system for correcting the fault of an airflow signal of a gasoline engine. The correction system consists of a fuzzy-model-based airflow estimation system and an airflow-monitoring system. The airflow estimation system estimates the normal airflow based on fuzzy neural networks and trained using the steepest-descent method and back-propagation algorithm. The airflow-monitoring system corrects the fault airflow signal according to the correction law. In order to raise the training performance, a genetic algorithm is used to search the learning rates and initial consequent gains of the fuzzy neural networks. The estimated results indicate that using a genetic algorithm certainly improves the performance of normal airflow estimation. The corrected results indicate that the airflow correction system can provide a feasible means of carrying out airflow fault correction in gasoline engine systems.

Keywords

gasoline engine fault correction neural fuzzy air—fuel ratio

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Fault correction of an airflow signal in a gasoline engine system using a neural fuzzy scheme and genetic algorithm

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