Developing advanced design and synthesis of self-learning optimal intelligent active suspension systems. Artificial neural-based fuzzy modeling is applied to set up the neural-based fuzzy model based on the training data from the nonlinear half-car suspension system dynamics. Furthermore, a robust optimal fuzzy controller is designed based on the proposed fuzzy model to improve ride quality and support appropriate movement in suspension systems. Moreover, the development of self-learning optimal intelligent active suspension can not only absorb disturbance and shock, to adapt the model, the sensor and the actuator error but also cope with the parameter uncertainty with minimum power consumption. The simulation results also indicate the feasibility and the applicability of the designed controller.


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    Titel :

    Neural-network-based optimal fuzzy control design for half-car active suspension systems


    Beteiligte:
    Shinq-Jen Wu, (Autor:in) / Cheng-Tao Wu, (Autor:in) / Tsu-Tian Lee, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    875983 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Neural-network-based optimal fuzzy control design for half-car active suspension systems

    Wu, S.-j. / Wu, C.-t. / Lee, T.-t. et al. | British Library Conference Proceedings | 2005


    Optimal active suspension control based on a half-car model

    Krtolica, R. / Hrovat, D. | Tema Archiv | 1990


    Nonlinear backstepping control design of half-car active suspension systems

    Lin,J.S. / Huang,C.J. / Nat.Chi Nan Univ.,TW | Kraftfahrwesen | 2003