Motorcycle Lateral Dynamic Estimation and Lateral Tire-Road Forces Reconstruction Using Sliding Mode Observer

Hamid Slimi (1) , Hichem Arioui (1) , Said Mammar (1)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

Extensive researches have shown that most of road accidents occur as a result of driver errors. A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents. This observation is even more valid on motorcycle, recall that, last decade about 1600 french people are killed each year. Preventing such kind of accidents using vehicle control systems, requires certain input data concerning vehicle dynamic parameters and vehicle road interaction. Unfortunately, some parameters like tire-road forces and lateral speed, which have a major impact on vehicle dynamics, are difficult to measure. Therefore, this data must be estimated. Due to the system nonlinearities and unmodeled dynamics, an observer based on sliding technique is proposed in this paper. The estimation process method use the motorcycle dynamic response's and standard sensors cheap and easily-available. Observer performances are tested and compared to the motorcycle model responses in different simulation conditions, various initial conditions and measurement noise. The effectiveness of the present approach is make out through the simulation results and show its practical potential as a low-cost solution for calculating lateral-tire forces and motorcycle lateral speed in a accuarate way.

Mots clés: Sliding mode observer; motorcycle dynamics; tire-road contact; virtual sensors

Domaines: Automatique / Robotique

Hamid Slimi, Hichem Arioui, Said Mammar. Motorcycle Lateral Dynamic Estimation and Lateral Tire-Road Forces Reconstruction Using Sliding Mode Observer. IEEE Intelligent Transportation Systems Conference (ITSC 2013), Oct 2013, The Hague, Netherlands. pp.821--826, ⟨10.1109/ITSC.2013.6728294⟩. ⟨hal-00868207⟩