• 자동차안전학회지
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• 제14권2호
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• pp.26-38
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• 2022

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).

• 한국항행학회논문지
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• 제3권2호
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• pp.156-166
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• 1999

다중차량 추적시스템에서 칼만 필터는 차량을 추적하기 위하여 일반적으로 사용되는 예측 알고리듬이다. 칼만 필터는 제한된 조건에서 최적의 결과를 나타내는 좋은 특성이 있으나 계산량이 많아 다수의 차량을 실시간으로 추적해야 하는 다중차량 추적시스템에서의 구현은 다소 어려운 단점이 있다. 본 논문에서는 실시간 다중차량 추적시스템의 구현을 위해 비교적 계산이 간단한 순환최소자승 알고리듬을 횡구조의 필터에 적용한 적응 예측기를 도입한다. 칼만 필터를 이용한 추적시스템과 성능을 비교 분석하기 위하여 컴퓨터 그래픽 도구로 제작된 가상 연속영상과 실제 교차로에서 촬영한 동영상을 이용하였다. 모의실험 결과는 본 논문에서 제안한 다중차량 추적시스템이 전용하드웨어 없이 일반 개인용 컴퓨터 환경 하에서 초당 30프레임의 속도로 촬영한 영상의 차량을 실시간으로 추적하는데 사용될 수 있음을 보여준다.

• 자동차안전학회지
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• 제13권4호
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• pp.129-143
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• 2021

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.