Online vehicle mass estimation using recursive least squares and supervisory data extraction

Hosam K. Fathy, Dongsoo Kang, Jeffrey L. Stein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

97 Scopus citations

Abstract

This paper examines the online estimation of onroad vehicles' mass. It classifies existing estimators based on the dynamics they use for estimation and whether they are event-seeking or averaging. It then proposes an algorithm comparable to this literature in accuracy and speed, but unique in its minimal instrumentation needs and ability to provide conservative mass error estimates, in the 3σ sense. The algorithm builds on the simple idea, inspired by perturbation theory, that inertial dynamics dominate vehicle motion over certain types of maneuvers. A supervisory algorithm searches for those maneuvers, and feeds the resulting filtered data into recursive least squares-based mass estimator and conservative mass error estimator. Both simulation and field data demonstrate the viability of the resulting approach.

Original languageEnglish (US)
Title of host publication2008 American Control Conference, ACC
Pages1842-1848
Number of pages7
DOIs
StatePublished - Sep 30 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2008 American Control Conference, ACC
CountryUnited States
CitySeattle, WA
Period6/11/086/13/08

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Fathy, H. K., Kang, D., & Stein, J. L. (2008). Online vehicle mass estimation using recursive least squares and supervisory data extraction. In 2008 American Control Conference, ACC (pp. 1842-1848). [4586760] (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2008.4586760