A neural network methodology is developed for air-to-fuel (A/F) ratio control of automotive fuel-injection systems. The dynamics of internal combustion engines and fuel-injection systems are extremely nonlinear, impeding methodical application of control theories. Thus, the design of standard production controllers relies heavily upon calibration and look-up tables. A neural network-type controller is developed in this article for its function-approximation abilities and its learning and adaptive capabilities. A cerebellar model articulation controller (CMAC) neural network is implemented in a research automobile to demonstrate the feasibility of this control architecture. Experimental results show that the CMAC fuel-injection controller is very effective in learning the engine nonlinearities and in dealing with the significant time-delays inherent in engine sensors.


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    Title :

    Neural network control of automotive fuel-injection systems


    Additional title:

    Regelungssystem auf der Basis eines neuronalen Netzes für die Kraftstoffeinspritzung


    Contributors:
    Majors, M. (author) / Stori, J. (author) / Cho, Dong-il (author)

    Published in:

    Publication date :

    1994


    Size :

    6 Seiten, 8 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Neural Network Control of Automotive Fuel-Injection Systems

    Majors, M. / Stori, J. / Cho, D.-I. | British Library Online Contents | 1994


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