This paper presents an intelligent fuzzy neural sliding mode control system (IFNSMS) for achieving intelligent car-following behavior. The IFNSMS comprises the self-learning fuzzy neural network-based (SLFNN) controller and the supervisory controller. The SLFNN is used as the main controller to mimic the ideal controller for achieving satisfactory vehicle following and the supervisory controller is incorporated with the SLFNN for achieving robust performance that against to imperfection of vehicle dynamics estimation and disturbance. To overcome the cumbersome in FNN design, a network self-learning mechanism is proposed such that the SLFNN determines the nodes with efficiency. Without the need to preliminary controlled system knowledge, the proposed structure learning demonstrates the properties of node-generating and node-reorganizing automatically. In the IFNSMS, all the parameters are evolved by adaptive algorithms. By tuning the structure and parameter simultaneously, a computational intelligence approach for adaptive vehicle collision avoidance is presented. To investigate the capabilities of the proposed approach, the IFNSMS is applied to a longitudinal car-following model. Through the simulation results the advantages of the proposed IFNSMS can be observed.


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    Title :

    Intelligent fuzzy neural sliding mode control for vehicle longitudinal collision avoidance


    Contributors:


    Publication date :

    2008


    Size :

    6 Seiten, 8 Bilder, 2 Tabellen, 17 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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