Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares

Manuel Acosta, Stratis Kanarachos

    Research output: Contribution to journalArticlepeer-review

    43 Citations (Scopus)
    1428 Downloads (Pure)

    Abstract

    This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire–road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker®. The results confirm its good and robust performance.

    Original languageEnglish
    Pages (from-to)3445–3465
    Number of pages21
    JournalNeural Computing and Applications
    Volume30
    Issue number11
    Early online date13 Mar 2017
    DOIs
    Publication statusPublished - Dec 2018

    Bibliographical note

    The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-017-2932-9

    Keywords

    • Grip potential estimation
    • Hybrid observer
    • Neural Networks
    • Tire force estimation

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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