Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Development of a RLS based Adaptive Sliding Mode Observer for Unknown Fault Reconstruction of Longitudinal Autonomous Driving

종방향 자율주행의 미지 고장 재건을 위한 순환 최소 자승 기반 적응형 슬라이딩 모드 관측기 개발

  • 오세찬 (한경대학교 ICT로봇기계공학부) ;
  • 송태준 (한경대학교 ICT로봇기계공학부) ;
  • 이종민 (서울대학교 기계항공공학부) ;
  • 오광석 (한경대학교 ICT로봇기계공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2020.08.12
  • Accepted : 2021.01.29
  • Published : 2021.03.31
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Abstract

This paper presents a RLS based adaptive sliding mode observer (A-SMO) for unknown fault reconstruction in longitudinal autonomous driving. Securing the functional safety of autonomous vehicles from unexpected faults of sensors is essential for avoidance of fatal accidents. Because the magnitude and type of the faults cannot be known exactly, the RLS based A-SMO for unknown acceleration fault reconstruction has been designed with relationship function in this study. It is assumed that longitudinal acceleration of preceding vehicle can be obtained by using the V2V (Vehicle to Vehicle) communication. The kinematic model that represents relative relation between subject and preceding vehicles has been used for fault reconstruction. In order to reconstruct fault signal in acceleration, the magnitude of the injection term has been adjusted by adaptation rule designed based on MIT rule. The proposed A-SMO in this study was developed in Matlab/Simulink environment. Performance evaluation has been conducted using the commercial software (CarMaker) with car-following scenario and evaluation results show that maximum reconstruction error ratios exist within range of ±10%.

Keywords

Acknowledgement

본 연구는 정부(미래창조과학부)의 재원(NRF-2016R1E1A1A01943543)으로 한국연구재단의 지원을 받아 수행된 사업이다.

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