Iterative learning identification for an automated off-highway vehicle

Nanjun Liu, Andrew G Alleyne

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

Abstract

This paper presents a new approach for identifying the lateral dynamics of an automated off-highway agricultural vehicle. A second order model is proposed to represent the vehicle lateral dynamics. An Iterative Learning Identification (ILI) method is used to identify the model parameters. Simulation and experimental results show the convergence of parameters with arbitrarily chosen initial estimations. The estimation results are compared to other traditional identification methods: least square estimation and gradient based adaptive estimation. The results highlight the practical benefit of the ILI approach- i.e. that it can be performed in a relatively small section of field and therefore done prior to actual usage or engagement with crops.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages4299-4304
Number of pages6
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Liu, N., & Alleyne, A. G. (2011). Iterative learning identification for an automated off-highway vehicle. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 4299-4304). [5991443] (Proceedings of the American Control Conference).