Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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109 Downloads (Pure)

Abstract

This paper deals with the automated design of a Virtual Sensor used to estimate the vehicle planar motion states and the axle lateral forces. It is proposed to substitute the cumbersome and non-trivial manual task of tuning a Kalman Filter by using meta-heuristic optimization, and in particular, employing the contrast-based Fruit Fly Optimization Algorithm (c-FOA). c-FOA is a recently developed powerful Swarm Intelligence meta-heuristic. The optimized state estimator is implemented in the vehicle dynamics simulation software IPG – CarMaker® and its performance is evaluated under aggressive maneuvers. Results are compared to those obtained with a filter tuned manually in previous stages of this research using a systematic trial and error method.

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Original languageEnglish
Title of host publication43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages4074-4079
Number of pages6
ISBN (Electronic)978-1-5386-1127-2, 978-1-5386-1126-5
ISBN (Print)978-1-5386-1128-9
DOIs
Publication statusPublished - 18 Dec 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society - China National Convention Center, Beijing, China
Duration: 29 Oct 20171 Nov 2017

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society
Abbreviated titleIECON 2017
CountryChina
CityBeijing
Period29/10/171/11/17
OtherThe scope of IECON 2017 is in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics.

Keywords

  • Mathematical model
  • Optimization
  • Tires
  • Kalman filters
  • Vehicle dynamics
  • Force
  • Axles
  • Tire Force Estimation
  • Extended Kalman Filter
  • Neural Networks
  • Fruit Fly Optimization

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  • Cite this

    Kanarachos, S., Acosta, M., & Fitzpatrick, M. (2017). Optimized Tire Force Estimation using Extended Kalman Filter and Fruit Fly Optimization. In 43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 4074-4079). IEEE. https://doi.org/10.1109/IECON.2017.8216698