Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Actuator Fault Detection and Adaptive Fault-Tolerant Control Algorithms Using Performance Index and Human-Like Learning for Longitudinal Autonomous Driving

종방향 자율주행을 위한 성능 지수 및 인간 모사 학습을 이용하는 구동기 고장 탐지 및 적응형 고장 허용 제어 알고리즘

  • 오세찬 (한경대학교 ICT로봇기계공학부) ;
  • 이종민 (서울대학교 기계항공공학부) ;
  • 오광석 (한경대학교 ICT로봇기계공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2021.05.17
  • Accepted : 2021.09.07
  • Published : 2021.12.31
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Abstract

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

Keywords

Acknowledgement

본 연구는 정부(미래창조과학부)의 재원(NRF-2016R1E1A1A01943543)으로 한국 연구재단의 지원을 받아 수행된 사업임.

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