# Contributions for parametric identification and observation of powered two-wheeler vehicles

Abstract : Nowadays, Powered Two-Wheeled Vehicles (PTWV) are an increasingly popular means of transport in daily urban and rural displacements, especially for the possibilities it offers to avoid traffic congestion. However, riders are considered as the most vulnerable road users. In fact, the risk of being killed in an accident is $29$ times higher for a motorcycle than for a driver of a four wheeled vehicle. Therewith, the unstable nature of the PTWV makes them more susceptible to control loss. This problem is even more complex during emergency braking or on cornering maneuvers. As matter of fact, passive and active safety systems (Anti-Lock Braking (ABS), Electronic Stability Control (ESP), seat belts, airbags) developed in favour of passenger vehicles have largely contributed to the reduction of risks on the road. However, the delay in the development of security systems for motorcycles is notable. Moreover, despite some existing systems, motorcycle riders use them badly or they don't use them at all. Therefore, it is not trivial that this delay, in the development of Advanced Rider Assistance Systems (ARAS), coming from a delay in the development of theoretical and research tools. This thesis fits into the context of designing ARAS for PTWV that can alert riders upstream of dangerous driving situations. Our work deals with observation and identification techniques to estimate the PTWV dynamic states and physical parameters. These latter are fundamental for risk quantification in ARAS design and to assess the safety of the PTWV, which are the main focus of our research work. The first part of the thesis concerns classical identification techniques to estimate physical parameters of PTWV. The second part deals with model-based observers implemented to estimate the dynamic states of the PTWV. We proposed an unknown input observer (UIO) for steering and road geometry estimation and an interconnected fuzzy observer (IFO) for both longitudinal and lateral dynamics. An alternative methods for identification algorithms are observer based identifier which provide both parameters identification and states estimation. Therefore, a Luenberger adaptive observer (LAO) to estimate lateral dynamic states and pneumatic stiffness as well as a delayed unknown inputs observer (DUIO) with an arbitrary relative degree, have been developed in this thesis. As matter of fact, all these techniques allow to estimate the vehicle dynamics while reducing the number of sensors and overcoming the problem of non-measurable states and parameters. These proposed methods require a simple combination of sensors and take into account realistic assumption like the longitudinal speed variation. Among others, this manuscript introduces a self calibration algorithm for Inertial Measurement Units (IMUs) alignment. Such a self-calibration method is used for telematic boxes (e-Boxes) installed on two-wheeled vehicles, whose IMUs’ axes are often result not to be aligned with the vehicle reference system. Finally, objective indicators are setting up to quantify riding risks. These functions were studied for ARAS purpose. To highlight the performance of these approaches, we have acquired data from high-fidelity motorcycle simulator and also with data from real motorcycles. To sum up, a comparison tables are drawn up for all the presented approaches. The results of both the numerical simulations and the performed experimentations seem to be quite promising.
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Cited literature [220 references]

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Majda Fouka. Contributions for parametric identification and observation of powered two-wheeler vehicles. Automatic. Université Paris-Saclay, Université d'Evry Val-d'Essonne, 2019. English. ⟨NNT : 2019SACLE033⟩. ⟨tel-02453899⟩

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