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http://dx.doi.org/10.22680/kasa2021.13.1.014

Development of a RLS based Adaptive Sliding Mode Observer for Unknown Fault Reconstruction of Longitudinal Autonomous Driving  

Oh, Sechan (한경대학교 ICT로봇기계공학부)
Song, Taejun (한경대학교 ICT로봇기계공학부)
Lee, Jongmin (서울대학교 기계항공공학부)
Oh, Kwangseok (한경대학교 ICT로봇기계공학부)
Yi, Kyongsu (서울대학교 기계항공공학부)
Publication Information
Journal of Auto-vehicle Safety Association / v.13, no.1, 2021 , pp. 14-25 More about this Journal
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
Sliding mode observer; Autonomous driving; Fault reconstruction; Recursive least squares; MIT rule;
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