Velocity Estimation Algorithms for Suspensions

DIana Hernandez-Alcantara; Luis Amezquita-Brooks; Nancy Morales-Villarreal; Omar A. Juarez-Tamez

doi:10.1109/GlobalSIP45357.2019.8969344

Velocity Estimation Algorithms for Suspensions

DIana Hernandez-Alcantara, Luis Amezquita-Brooks, Nancy Morales-Villarreal, Omar A. Juarez-Tamez

Departamento de Física y Matemáticas

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

Semi-active suspension systems aim to improve stability and comfort of vehicles. The most well-known semi-active suspension control strategies assume knowledge of the vehicle vertical-velocities. However, the vehicle normally does not includes sensors for these variables. In this paper various vertical-velocities estimation algorithms are compared: the traditional filtered integration algorithm, the classical Luenberger observer, the Unknown Input Observer and the Kalman Filter. A comprehensive comparison between the estimation methods in several perturbation scenarios is performed. The results show that it is possible to archive good estimation performance with a low level of complexity using a combination of estimators.

Original language	English
DOIs	https://doi.org/10.1109/GlobalSIP45357.2019.8969344
Publication status	Published - 1 Nov 2019
Event	7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada Duration: 11 Nov 2019 → 14 Nov 2019

Conference

Conference	7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
Country/Territory	Canada
City	Ottawa
Period	11/11/19 → 14/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Information Systems
Information Systems and Management
Artificial Intelligence
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/GlobalSIP45357.2019.8969344

Cite this

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title = "Velocity Estimation Algorithms for Suspensions",

abstract = "Semi-active suspension systems aim to improve stability and comfort of vehicles. The most well-known semi-active suspension control strategies assume knowledge of the vehicle vertical-velocities. However, the vehicle normally does not includes sensors for these variables. In this paper various vertical-velocities estimation algorithms are compared: the traditional filtered integration algorithm, the classical Luenberger observer, the Unknown Input Observer and the Kalman Filter. A comprehensive comparison between the estimation methods in several perturbation scenarios is performed. The results show that it is possible to archive good estimation performance with a low level of complexity using a combination of estimators.",

author = "DIana Hernandez-Alcantara and Luis Amezquita-Brooks and Nancy Morales-Villarreal and Juarez-Tamez, {Omar A.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 ; Conference date: 11-11-2019 Through 14-11-2019",

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AU - Hernandez-Alcantara, DIana

AU - Amezquita-Brooks, Luis

AU - Morales-Villarreal, Nancy

AU - Juarez-Tamez, Omar A.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Semi-active suspension systems aim to improve stability and comfort of vehicles. The most well-known semi-active suspension control strategies assume knowledge of the vehicle vertical-velocities. However, the vehicle normally does not includes sensors for these variables. In this paper various vertical-velocities estimation algorithms are compared: the traditional filtered integration algorithm, the classical Luenberger observer, the Unknown Input Observer and the Kalman Filter. A comprehensive comparison between the estimation methods in several perturbation scenarios is performed. The results show that it is possible to archive good estimation performance with a low level of complexity using a combination of estimators.

AB - Semi-active suspension systems aim to improve stability and comfort of vehicles. The most well-known semi-active suspension control strategies assume knowledge of the vehicle vertical-velocities. However, the vehicle normally does not includes sensors for these variables. In this paper various vertical-velocities estimation algorithms are compared: the traditional filtered integration algorithm, the classical Luenberger observer, the Unknown Input Observer and the Kalman Filter. A comprehensive comparison between the estimation methods in several perturbation scenarios is performed. The results show that it is possible to archive good estimation performance with a low level of complexity using a combination of estimators.

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M3 - Paper

T2 - 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019

Y2 - 11 November 2019 through 14 November 2019

ER -

Velocity Estimation Algorithms for Suspensions

Abstract

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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