Quasi-LPV Unknown Input Observer with Nonlinear Outputs: Application to Motorcycles

Lamri Nehaoua (1) , Majda Fouka (1) , Hichem Arioui (1)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

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.

Mots clés: Nonlinear Output; Quasi-LPV Observer; ISS Stability; Motorcycle Lateral Dynamics

Domaines: Automatique / Robotique

Lamri Nehaoua, Majda Fouka, Hichem Arioui. Quasi-LPV Unknown Input Observer with Nonlinear Outputs: Application to Motorcycles. IEEE International Conference on Robotics and Automation (ICRA 2021), May 2021, Xi'an, China. pp.9312--9317, ⟨10.1109/ICRA48506.2021.9560814⟩. ⟨hal-03503922⟩