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http://dx.doi.org/10.22680/kasa2022.14.2.026

Development of Multiple RLS and Actuator Performance Index-based Adaptive Actuator Fault-Tolerant Control and Detection Algorithms for Longitudinal Autonomous Driving  

Oh, Sechan (한경대학교 ICT로봇기계공학부)
Lee, Jongmin (서울대학교 기계항공공학부)
Oh, Kwangseok (한경대학교 ICT로봇기계공학부)
Yi, Kyongsu (서울대학교 기계항공공학부)
Publication Information
Journal of Auto-vehicle Safety Association / v.14, no.2, 2022 , pp. 26-38 More about this Journal
Abstract
This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).
Keywords
Feedback control; Autonomous driving; Multiple RLS; Fault tolerant control; Performance index;
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