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

Manuel Acosta, Stratis Kanarachos

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-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 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 - Sept 2018
    EventInternational Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems - Madrid, Spain
    Duration: 13 Sept 201715 Sept 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
    Country/TerritorySpain
    CityMadrid
    Period13/09/1715/09/17
    Internet address

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