Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization

Stratis Kanarachos; Manuel Acosta; Michael Fitzpatrick

doi:10.1109/IECON.2017.8216698

Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization

Stratis Kanarachos, Manuel Acosta, Michael Fitzpatrick

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

303 Downloads (Pure)

Abstract

This paper deals with the automated design of a Virtual Sensor used to estimate the vehicle planar motion states and the axle lateral forces. It is proposed to substitute the cumbersome and non-trivial manual task of tuning a Kalman Filter by using meta-heuristic optimization, and in particular, employing the contrast-based Fruit Fly Optimization Algorithm (c-FOA). c-FOA is a recently developed powerful Swarm Intelligence meta-heuristic. The optimized state estimator is implemented in the vehicle dynamics simulation software IPG – CarMaker® and its performance is evaluated under aggressive maneuvers. Results are compared to those obtained with a filter tuned manually in previous stages of this research using a systematic trial and error method.

Publisher Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Original language	English
Title of host publication	43rd Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Pages	4074-4079
Number of pages	6
ISBN (Electronic)	978-1-5386-1127-2, 978-1-5386-1126-5
ISBN (Print)	978-1-5386-1128-9
DOIs	https://doi.org/10.1109/IECON.2017.8216698
Publication status	Published - 18 Dec 2017
Event	43rd Annual Conference of the IEEE Industrial Electronics Society - China National Convention Center, Beijing, China Duration: 29 Oct 2017 → 1 Nov 2017

Conference

Conference	43rd Annual Conference of the IEEE Industrial Electronics Society
Abbreviated title	IECON 2017
Country/Territory	China
City	Beijing
Period	29/10/17 → 1/11/17
Other	The scope of IECON 2017 is in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics.

Keywords

Mathematical model
Optimization
Tires
Kalman filters
Vehicle dynamics
Force
Axles
Tire Force Estimation
Extended Kalman Filter
Neural Networks
Fruit Fly Optimization

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10.1109/IECON.2017.8216698

Post-PrintAccepted author manuscript, 1.33 MB

Cite this

@inproceedings{f2c278f52572424584833ea164262564,

title = "Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization",

abstract = "This paper deals with the automated design of a Virtual Sensor used to estimate the vehicle planar motion states and the axle lateral forces. It is proposed to substitute the cumbersome and non-trivial manual task of tuning a Kalman Filter by using meta-heuristic optimization, and in particular, employing the contrast-based Fruit Fly Optimization Algorithm (c-FOA). c-FOA is a recently developed powerful Swarm Intelligence meta-heuristic. The optimized state estimator is implemented in the vehicle dynamics simulation software IPG – CarMaker{\textregistered} and its performance is evaluated under aggressive maneuvers. Results are compared to those obtained with a filter tuned manually in previous stages of this research using a systematic trial and error method.Publisher Statement: {\textcopyright} 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",

keywords = "Mathematical model, Optimization, Tires, Kalman filters, Vehicle dynamics, Force, Axles, Tire Force Estimation, Extended Kalman Filter, Neural Networks, Fruit Fly Optimization",

author = "Stratis Kanarachos and Manuel Acosta and Michael Fitzpatrick",

year = "2017",

month = dec,

day = "18",

doi = "10.1109/IECON.2017.8216698",

language = "English",

isbn = "978-1-5386-1128-9",

pages = "4074--4079",

booktitle = "43rd Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

address = "United States",

note = "43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 ; Conference date: 29-10-2017 Through 01-11-2017",

}

TY - GEN

T1 - Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization

AU - Kanarachos, Stratis

AU - Acosta, Manuel

AU - Fitzpatrick, Michael

PY - 2017/12/18

Y1 - 2017/12/18

N2 - This paper deals with the automated design of a Virtual Sensor used to estimate the vehicle planar motion states and the axle lateral forces. It is proposed to substitute the cumbersome and non-trivial manual task of tuning a Kalman Filter by using meta-heuristic optimization, and in particular, employing the contrast-based Fruit Fly Optimization Algorithm (c-FOA). c-FOA is a recently developed powerful Swarm Intelligence meta-heuristic. The optimized state estimator is implemented in the vehicle dynamics simulation software IPG – CarMaker® and its performance is evaluated under aggressive maneuvers. Results are compared to those obtained with a filter tuned manually in previous stages of this research using a systematic trial and error method.Publisher Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

AB - This paper deals with the automated design of a Virtual Sensor used to estimate the vehicle planar motion states and the axle lateral forces. It is proposed to substitute the cumbersome and non-trivial manual task of tuning a Kalman Filter by using meta-heuristic optimization, and in particular, employing the contrast-based Fruit Fly Optimization Algorithm (c-FOA). c-FOA is a recently developed powerful Swarm Intelligence meta-heuristic. The optimized state estimator is implemented in the vehicle dynamics simulation software IPG – CarMaker® and its performance is evaluated under aggressive maneuvers. Results are compared to those obtained with a filter tuned manually in previous stages of this research using a systematic trial and error method.Publisher Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

KW - Mathematical model

KW - Optimization

KW - Tires

KW - Kalman filters

KW - Vehicle dynamics

KW - Force

KW - Axles

KW - Tire Force Estimation

KW - Extended Kalman Filter

KW - Neural Networks

KW - Fruit Fly Optimization

U2 - 10.1109/IECON.2017.8216698

DO - 10.1109/IECON.2017.8216698

M3 - Conference proceeding

SN - 978-1-5386-1128-9

SP - 4074

EP - 4079

BT - 43rd Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE

T2 - 43rd Annual Conference of the IEEE Industrial Electronics Society

Y2 - 29 October 2017 through 1 November 2017

ER -

Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization

Abstract

Conference

Keywords

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Cite this