Adaptive Observer for Motorcycle State Estimation and Tire Cornering Stiffness Identification

Majda Fouka (1) , Lamri Nehaoua (1) , Mohammed El Habib Dabladji (1) , Hichem Arioui (1) , Saïd Mammar (1)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

In this paper, a linear parameter varying (LPV) adaptive observer is designed for state estimation and tire cornering stiffness identification based on lateral motorcycle model. The estimation is based on a general Lipstchitz condition, Lyapunov function and is subjected to persistency of excitation conditions. Further, the LPV observer is transformed into Takagi-Sugeno (T-S) fuzzy observer and sufficient conditions, for the existence of the estimator, are given in terms of linear matrix inequalities (LMIs). This method is designed assuming that some of the states are not available, since parametric identification is generally developed assuming that all the system states are available (measured or estimated). Finally, the effectiveness of the proposed estimation method is illustrated through test scenarios performed with the well-known motorcycle simulator 'BikeSim' and by field test using data measurement carried out on experimental motorcycle.

Mots clés: Estimation; LMI; Luenberger Observer; Motorcycle; TS model; Index Terms-Motorcycle

Domaines: Automatique / Robotique

Majda Fouka, Lamri Nehaoua, Mohammed El Habib Dabladji, Hichem Arioui, Saïd Mammar. Adaptive Observer for Motorcycle State Estimation and Tire Cornering Stiffness Identification. 57th IEEE Conference on Decision and Control (CDC 2018 ), Dec 2018, Miami Beach, FL, United States. pp.3018-3024, ⟨10.1109/CDC.2018.8619637⟩. ⟨hal-02063555⟩