$H_{\infty}$ memory observer design for vehicle suspension state estimation and unknown road reconstruction

Gang Wang (1) , Mohammed Chadli (2) , Saïd Mammar (2)

1 School of Mechanical Engineering
2 IBISC - Informatique, BioInformatique, Systèmes Complexes

This brief is concerned with the state estimation problem for a vehicle suspension subjected to unknown road input. Limited by installation space and number of sensors, the measurable states are limited. To estimate the entire suspension states and road profile simultaneously, an H∞ memory observer (HMO) is developed. Unlike the traditional unknown input observer (UIO) designed to the suspension system, the proposed HMO takes advantage of the memory outputs. Disturbance decoupling and H∞ attenuation techniques are used in the design. Furthermore, a sufficient condition based on LMI framework is provided to find the observer gains. The simulation results show that the HMO is efficient and the estimated values are very close to the real ones.

Mots clés: H∞ memory observer; Road reconstruction; Suspension state estimation; Unknown input observer

Domaines: Automatique / Robotique

Gang Wang, Mohammed Chadli, Saïd Mammar. $H_{\infty}$ memory observer design for vehicle suspension state estimation and unknown road reconstruction. 28th Mediterranean Conference on Control and Automation (MED 2020), Sep 2020, Saint-Raphaël, France. pp.479-483, ⟨10.1109/MED48518.2020.9183121⟩. ⟨hal-02970533⟩