Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter

Manuel Acosta; Stratis Kanarachos

Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter

Manuel Acosta, Stratis Kanarachos

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

Abstract

This paper presents an Optimized Unscented Kalman Filter for vehicle dynamics virtual sensing. An automated procedure to optimize the virtual sensor parameters based on metaheuristic algorithms is presented in order to avoid the time-consuming and complex manual tuning task. Specifically, Genetic Algorithm Optimization (GA) and contrast-based Fruit Fly optimization (c-FOA) are applied to minimize the estimation error in steady-state and transient driving maneuvers. The virtual sensor is implemented in a high-fidelity vehicle dynamics simulation software (IPG-CarMaker ®) and results demonstrate the improvement of the estimation accuracy with respect to a preliminary filter tuning carried out using a systematic trial and error approach.

Original language	English
Title of host publication	Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems
Publisher	Springer Verlag
Pages	275-290
ISBN (Electronic)	978-3-319-89890-2
ISBN (Print)	978-3-319-89889-6
Publication status	Published - Sept 2018
Event	International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems - Madrid, Spain Duration: 13 Sept 2017 → 15 Sept 2017 http://eurogen2017.etsiae.upm.es/

Publication series

Name	Computational Methods in Applied Sciences
Volume	49

Conference

Conference	International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems
Abbreviated title	EUROGEN 2017
Country/Territory	Spain
City	Madrid
Period	13/09/17 → 15/09/17
Internet address	http://eurogen2017.etsiae.upm.es/

1 Paper

Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter
Acosta, M. & Kanarachos, S., 1 Sept 2018.
Research output: Contribution to conference › Paper › peer-review

Cite this

Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter. / Acosta, Manuel; Kanarachos, Stratis.
Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Springer Verlag, 2018. p. 275-290 (Computational Methods in Applied Sciences; Vol. 49).

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

Acosta, M & Kanarachos, S 2018, Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter. in Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol. 49, Springer Verlag, pp. 275-290, International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems, Madrid, Spain, 13/09/17.

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title = "Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter",

abstract = "This paper presents an Optimized Unscented Kalman Filter for vehicle dynamics virtual sensing. An automated procedure to optimize the virtual sensor parameters based on metaheuristic algorithms is presented in order to avoid the time-consuming and complex manual tuning task. Specifically, Genetic Algorithm Optimization (GA) and contrast-based Fruit Fly optimization (c-FOA) are applied to minimize the estimation error in steady-state and transient driving maneuvers. The virtual sensor is implemented in a high-fidelity vehicle dynamics simulation software (IPG-CarMaker {\textregistered}) and results demonstrate the improvement of the estimation accuracy with respect to a preliminary filter tuning carried out using a systematic trial and error approach.",

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AB - This paper presents an Optimized Unscented Kalman Filter for vehicle dynamics virtual sensing. An automated procedure to optimize the virtual sensor parameters based on metaheuristic algorithms is presented in order to avoid the time-consuming and complex manual tuning task. Specifically, Genetic Algorithm Optimization (GA) and contrast-based Fruit Fly optimization (c-FOA) are applied to minimize the estimation error in steady-state and transient driving maneuvers. The virtual sensor is implemented in a high-fidelity vehicle dynamics simulation software (IPG-CarMaker ®) and results demonstrate the improvement of the estimation accuracy with respect to a preliminary filter tuning carried out using a systematic trial and error approach.

M3 - Chapter

SN - 978-3-319-89889-6

T3 - Computational Methods in Applied Sciences

SP - 275

EP - 290

BT - Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems

PB - Springer Verlag

T2 - International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems

Y2 - 13 September 2017 through 15 September 2017

ER -

Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter

Abstract

Publication series

Conference

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Research output

Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter

Cite this