Estimation of tire forces, road grade, and road bank angle using tire model-less approaches and Fuzzy Logic

Manuel Acosta, A. Alatorre, Stratis Kanarachos, A. Victorino, A. Charara

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

This paper presents a modular observer structure to estimate the tire-road forces robustly, avoiding the use of any particular tire model, and using standard signals available in current passenger vehicles. The observer consists of a feedforward longitudinal force estimation block and a hybrid lateral force estimation module formed by an Extended Kalman Filter and a Static Neural Network Structure. Road grade and bank angle are estimated using sensor fusion, where a Fuzzy Logic controller combines the outputs from a Euler Kinematic model and a Recursive Least Squares block. The proposed observer is tested and verified using the simulation software IPG CarMaker® under realistic driving situations. Lastly, the feasibility of the longitudinal force block is proved with real-time experiments.
Original languageEnglish
Pages14836-14842
Number of pages7
Publication statusPublished - 1 Jul 2017
EventThe 20th World Congress of the International Federation of Automatic Control - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
https://www.ifac2017.org/

Conference

ConferenceThe 20th World Congress of the International Federation of Automatic Control
Abbreviated titleIFAC 2017
CountryFrance
CityToulouse
Period9/07/1714/07/17
Internet address

Keywords

  • Neural Networks
  • tire force estimation
  • bank angle
  • road grade

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    Acosta, M., Alatorre, A., Kanarachos, S., Victorino, A., & Charara, A. (2017). Estimation of tire forces, road grade, and road bank angle using tire model-less approaches and Fuzzy Logic. 14836-14842. Paper presented at The 20th World Congress of the International Federation of Automatic Control, Toulouse, France.