Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter

Manuel Acosta; Stratis Kanarachos

doi:10.1007/978-3-319-89890-2_18

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

2 Citations (Scopus)

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	Computational Methods in Applied Sciences
Editors	Esther Andrés-Pérez, Leo M. González, Jacques Periaux, Nicolas Gauger, Domenico Quagliarella, Kyriakos Giannakoglou
Publisher	Springer
Pages	275-290
Number of pages	16
Edition	1
ISBN (Electronic)	978-3-319-89890-2
ISBN (Print)	978-3-319-89889-6
DOIs	https://doi.org/10.1007/978-3-319-89890-2_18
Publication status	Published - 7 Sept 2018

Publication series

Name	Computational Methods in Applied Sciences
Volume	49
ISSN (Print)	1871-3033

ASJC Scopus subject areas

Civil and Structural Engineering
Modelling and Simulation
Biomedical Engineering
Computer Science Applications
Fluid Flow and Transfer Processes
Computational Mathematics
Electrical and Electronic Engineering

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10.1007/978-3-319-89890-2_18

Cite this

Acosta, M., & Kanarachos, S. (2018). Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter. In E. Andrés-Pérez, L. M. González, J. Periaux, N. Gauger, D. Quagliarella, & K. Giannakoglou (Eds.), Computational Methods in Applied Sciences (1 ed., pp. 275-290). (Computational Methods in Applied Sciences; Vol. 49). Springer. https://doi.org/10.1007/978-3-319-89890-2_18

Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter. / Acosta, Manuel; Kanarachos, Stratis.
Computational Methods in Applied Sciences. ed. / Esther Andrés-Pérez; Leo M. González; Jacques Periaux; Nicolas Gauger; Domenico Quagliarella; Kyriakos Giannakoglou. 1. ed. Springer, 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 E Andrés-Pérez, LM González, J Periaux, N Gauger, D Quagliarella & K Giannakoglou (eds), Computational Methods in Applied Sciences. 1 edn, Computational Methods in Applied Sciences, vol. 49, Springer, pp. 275-290. https://doi.org/10.1007/978-3-319-89890-2_18

Acosta M, Kanarachos S. Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter. In Andrés-Pérez E, González LM, Periaux J, Gauger N, Quagliarella D, Giannakoglou K, editors, Computational Methods in Applied Sciences. 1 ed. Springer. 2018. p. 275-290. (Computational Methods in Applied Sciences). doi: 10.1007/978-3-319-89890-2_18

Acosta, Manuel ; Kanarachos, Stratis. / Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter. Computational Methods in Applied Sciences. editor / Esther Andrés-Pérez ; Leo M. González ; Jacques Periaux ; Nicolas Gauger ; Domenico Quagliarella ; Kyriakos Giannakoglou. 1. ed. Springer, 2018. pp. 275-290 (Computational Methods in Applied Sciences).

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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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Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter

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