This study considers a new design methodology in the context of active vehicle suspensions control. The approach combines concepts from Stochastic Optimal Control with those of Learning Automate stochastic 'test-track' environment. For practical application, the overwhelming advantage of this approach is that no explicit modelling is required, and considerable time savings may be expected in system development. This simulation study considers the on-line learning of optimal control in a low-bandwidth active suspension system, where control feedback is confined to a body-mounted accelerometer at each corner of the vehicle. It is shown that learning can successfully take place under a range of conditions, including the case when there is substantial transducer noise. The performance of the resulting control system is shown to depend heavily on the nature of the learning environment.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Application of learning automata to controller design in slow-active automobile suspensions


    Additional title:

    Anwendung von Lernautomaten zum Regleraufbau bei langsamen aktiven Automobilfederungen


    Contributors:
    Marsh, C. (author) / Gordon, T.J. (author) / Wu, Q.H. (author)

    Published in:

    Vehicle System Dynamics ; 24 , 8 ; 597-616


    Publication date :

    1995


    Size :

    20 Seiten, 7 Bilder, 9 Tabellen, 9 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English