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.

The purpose of the present work is the reconstruction of motorcycle lateral dynamics. The main idea is to estimate pertinent states and unknown inputs (rider action) with respect to nonlinear outputs due to motion transformation frames (inertial sensors are away from the local frame). To overcome this issue, we propose a new Unknown Input Observers with variable output matrix. In this paper, we take into account the ground truth measurements provided in the body-fixed frame, parametric uncertainties as well as sensors noise. This step leads to a nonlinear parameter-dependent output equation with unmeasured premise variables in the observer design. The observer synthesis is specified in term of convergence and stability study by considering a quadratic Lyapunov function associated with the Input To State Stability (ISS) property. Sufficient conditions are agreed in terms of Linear Matrix Inequalities (LMIs). Finally, the performances, usefulness and robustness of the proposed approach are assessed throughout an electric Scooter under urban riding scenario.

Recent advanced riding assistance safety systems, such as electronic yaw stability control, adaptive cruise control, and lane-keeping systems, require good approximations of motorcycle inertial properties, such as yaw, roll and pitch moment of inertia, this parameter can vary significantly with the rider's weight and heavy baggage placed on the luggage rack. This paper presents further research on parametric identification of two wheeler vehicles, carried out using a recursive Levenberg-Marquardt parameter identification formulation. This approach needs the use of sensitivity functions to identify acceleration responses in time domain by updating coupled inertial parameters value. The identification algorithm is implemented in MATLAB / Simulink software. Data and prior value are taken from the professional motorcycle simulation software BikeSim (based on high fidelity virtual motorcycle models).

Powered Two-Wheeled vehicles (PTWv) are an increasingly popular means of transport. The cost and the risks of the development phase of this vehicles has to be diminished in order to ensure acceptable levels of comfort and safety for riders upstream of hazardous driving situations. It is required to study motorcycle while riding in cornering and lane-keeping to interpret the dynamics behavior. Thus, we need to obtain dynamic model that tightly adjusts to the real lateral behavior of the motorcycle, in the way that it will lead to precise simulation and experimental results. A technique for cascade identification of parameters based on data and optimization algorithm, is presented here. This methodology makes profit of the possibility given by this type of algorithm for solving multiple objectives function consecutively. After the identification method is outlined, simulations and experimental results are presented in order to confirm the accuracy of the parameters estimation under the persistent condition of the inputs.