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

Manuel Acosta, Stratis Kanarachos

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish
Title of host publicationEvolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems
PublisherSpringer Verlag
Pages275-290
ISBN (Electronic)978-3-319-89890-2
ISBN (Print)978-3-319-89889-6
Publication statusPublished - Sep 2018
EventInternational Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems - Madrid, Spain
Duration: 13 Sep 201715 Sep 2017
http://eurogen2017.etsiae.upm.es/

Publication series

NameComputational Methods in Applied Sciences
Volume49

Conference

ConferenceInternational Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems
Abbreviated titleEUROGEN 2017
CountrySpain
CityMadrid
Period13/09/1715/09/17
Internet address

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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 (pp. 275-290). (Computational Methods in Applied Sciences; Vol. 49). Springer Verlag.