This paper deals with an observer-based identification framework to estimate both lateral dynamics states and tires’ cornering parameters in the perspective of designing advanced rider assistance systems for powered two-wheeled vehicle. An adaptive observer is proposed to reconstruct the state variables regardless of the forward velocity variations and to estimate the real unknown tires’ parameters. The stability and convergence analysis of the proposed observer is based on the Lyapunov theory, the persistency of excitation and the general Lipschitz condition. To enable this observer design, the linear parameter varying observer is transformed into Takagi-Sugeno exact form where the sufficient conditions are given in terms of linear matrix inequalities. Finally, an evaluation framework is proposed to provide a critical overview of the method’s effectiveness. The proposed adaptive law is compared to direct estimation and dynamic inversion estimation methods. Co-simulation scenarios are performed by using both BikeSim© motorcycle simulator and real data-log obtained from an instrumented electrical scooter.


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    Title :

    Motorcycle State Estimation and Tire Cornering Stiffness Identification Applied to Road Safety: Using Observer-Based Identifiers


    Contributors:


    Publication date :

    2022-07-01


    Size :

    3936956 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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