A low bandwidth active suspension system is developed to minimize car body acceleration subject to the rattle-space constraint. The actuator is required to control both the body acceleration and the suspension deflection simultaneously and keep good maneuverability. They are conflicting in some portion of suspension cycle. Hence a powerful control approach is expected to achieve an optimal active suspension system with good riding comfort and maneuverability under the hardware suspension travel constraint. A decentralized adaptive neural controller (DANC) for active vehicle suspension systems using singular perturbation method is developed in this paper. In order to reduce the neural network (NN) weight's number, the DANC is designed based on the decentralized single input single output (SISO) NN. The active suspension system SIMO characteristic is decoupled into two time-scale separated SISO systems via the singular perturbation technique for achieving a compromise between riding comfort and handing performance. The structure of this MIMO model-free controller is derived from the Lyapunov stability theory to monitor the system for tracking a user-defined reference model. The experimental results are discussed and compared with that of the passive suspension.


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    Title :

    A decentralized adaptive neural controller for active vehicle suspension systems


    Additional title:

    Ein dezentraler adaptiver neuronaler Regler für aktive Fahrzeugaufhängungen


    Contributors:


    Publication date :

    2006


    Size :

    6 Seiten, 5 Bilder, 6 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





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