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 publicationComputational Methods in Applied Sciences
EditorsEsther Andrés-Pérez, Leo M. González, Jacques Periaux, Nicolas Gauger, Domenico Quagliarella, Kyriakos Giannakoglou
PublisherSpringer
Pages275-290
Number of pages16
Edition1
ISBN (Electronic)978-3-319-89890-2
ISBN (Print)978-3-319-89889-6
DOIs
Publication statusPublished - 7 Sep 2018

Publication series

NameComputational Methods in Applied Sciences
Volume49
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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  • 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