Electrohydraulic shift control of a vehicle automatic transmission has been predominantly carried out via open-loop control based on numerous time-consuming calibrations. Despite remarkable success in practice, the variations of system characteristics inevitably cause the performance of the tuned open-loop controller to deteriorate. As a result, the controller parameters need to be continuously updated in order to maintain satisfactory shift quality. This paper presents a selflearning algorithm for automatic transmission shift control in a commercial construction vehicle during the inertia phase. First, an observer reconstructs the turbine acceleration signal (impossible to measure in a commercial construction vehicle) from the readily accessible turbine speed measurement. Then, a control algorithm based on a quadratic cost function of the turbine acceleration is shown to guarantee the asymptotic convergence (within a specified target bound) of the error between the actual and the desired turbine accelerations. A Lyapunov argument plays a crucial role in deriving adaptive laws for control parameters. The simulation and hardware-in-the-loop simulation studies show that the proposed algorithm actually delivers the promise of satisfactory performance despite the variations and uncertainties of system characteristics.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-learning approach to automatic transmission shift control in a commercial construction vehicle during the inertia phase


    Beteiligte:
    Hahn, J-O (Autor:in) / Hur, J-W (Autor:in) / Choi, G-W (Autor:in) / Cho, Y M (Autor:in) / Lee, K I (Autor:in)


    Erscheinungsdatum :

    2002-11-01


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch