Nonlinear observer with observability-based parameter adaptation for vehicle motion estimation

verfasst von

Vicent Rodrigo Marco, Jens Kalkkuhl, Thomas Seel

Abstract

In the framework of self-driving cars and driver-assistance systems the demand for reliable information about the vehicle ego-motion is increasing. This paper describes an estimation scheme, based on a nonlinear observer design, that provides velocity and attitude angle estimates. The approach relies on a state-affine representation of a kinematic model bolstered by a dynamic model-based measurement equation. By means of a thorough observability analysis, global exponential convergence is theoretically guaranteed. Additionally, in order to minimize the errors introduced by the dynamic model limitations, an observer tuning rule is proposed. The adaptation of the tuning parameters is built upon an online observability assessment of the system without the support of the dynamic model. Experimental results show that the presented approach reliably estimates the motion states.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Artikel

Journal

IFAC-PapersOnLine

Band

51

Seiten

60-65

Anzahl der Seiten

6

ISSN

2405-8963

Publikationsdatum

2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2018.09.091 (Zugang: Offen)