Data Calibration and Quasi-LPV Unknown Input Observer: Powered Two-Wheeled Vehicle

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

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

This paper is dedicated to the powered two-wheeled vehicle (PTWV) lateral dynamics estimation. Differently from common unknown input observer (UIO) approaches reported in the literature, which considers constant output matrix and exact premise variables, this work takes into account the real measurement provided in the body-fixed frame. This consideration leads to a nonlinear parameter-dependent output equation with unmeasurable premise variables in the UIO design. The observer convergence and stability study are established by considering a quadratic Lyapunov function associated with the Input to State Stability (ISS) to guaranty boundedness of the state estimation errors. Sufficient conditions are given in terms of linear matrix inequalities (LMIs). Finally, the performances and applicability of the proposed approach are evaluated by co-simulation using BikeSim© high-fidelity motorcycle simulator.

Keywords: ISS stability; PTWV; Quasi-LPV Observer

Domains: Automatic

Madjda Fouka, Lamri Nehaoua, Hichem Arioui. Data Calibration and Quasi-LPV Unknown Input Observer: Powered Two-Wheeled Vehicle. 59th IEEE Conference on Decision and Control (CDC 2020), Dec 2020, Jeju Island, South Korea. pp.3921--3926, ⟨10.1109/CDC42340.2020.9304215⟩. ⟨hal-03130130⟩