Adaptive Sliding Mode Observer with an Adaptation Rule for the Injection Term for Disturbance Estimation Based on Robust Finite-Time Stability

본 논문에서는 강건 유한 시간 안정성 기반 외란 추정을 위한 주입항 적응 규칙을 이용하는 적응형 슬라이딩 모드 관측 알고리즘을 제안한다. 슬라이딩 모드 관측기의 오차 수렴 시간은 초기 추정 오차 값에 따라 달라질 수 있고, 추정 오차는 관측이 시작될 때 시스템의 상태량과 관측기의 초기 값에 의해 결정된다. 본 연구에서는 초기 추정 오차 값에 관계없이 강건한 유한 시간 안정성 확보를 위해 슬라이딩 모드 관측기의 주입항 적응 규칙을 설계하였다. 주입항의 적응 규칙은 비용 함수와 목표 수렴 시간을 정의함으로써 주입항 크기가 조정될 수 있도록 설계하였다. 본 연구에서 설계된 강건 유한 시간 안정성을 위한 적응형 슬라이딩 모드 관측기는 자동차의 종방향 주행 상황에서 계측된 실 주행 데이터를 이용해 성능 평가가 수행되었다. 제안된 적응형 관측기 알고리즘은 향후 관측기의 초기 오차에 관계 없이 강건 유한 시간 안정성 확보를 필요로 하는 다양한 기능 안전 시스템에 적용될 것으로 예상한다. An adaptive sliding mode observer with an adaptation rule for the injection term for disturbance estimation based on robust finite-time stability is presented. The convergence time of observers can be varied with the value of the initial estimation error, which is determined by the initial states of the observer and the system. To secure robust finite-time stability regardless of the initial estimation error, an adaptation rule for the injection term in the sliding mode observer has been designed. The adaptation rule has been designed so that the magnitude of the injection term is adjusted by the cost function and desired convergence time. A performance evaluation has been conducted using actual driving data obtained under a longitudinal driving situation of a vehicle. It is expected that the observer algorithm proposed in this study can be used for the design of functional safety systems that require robust finite-time convergence.N

Development of an Adaptive Feedback based Actuator Fault Detection and Tolerant Control Algorithms for Longitudinal Autonomous Driving

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.N

A fault level decision algorithm for functional safety of autonomous vehicles and longitudinal fault detection based performance evaluation

본 논문은 자율주행 시스템의 기능 안전을 위한 고장 단계 판단 알고리즘 개발 및 종방향 고장 탐지 기반 성능평가에 관한 연구이다. 본 연구에서 제안하는 자율주행 자동차의 고장-안전 시스템은 자율주행을 위해 사용되는 센서 및 구동기 고장에 대한 인지, 판단, 제어 단계로 구분되며 각 단계는 고장을 검출 및 분류, 고장 단계 판단, 판단에 따른 대처방안을 제시한다. 본 연구는 고장을 4단계로 구분함으로써 고장 단계를 정의 했으며, 고장으로부터 합리적인 대응 제어를 위한 판단 알고리즘을 제시한다. 고장 판단은 세부 판단 단계인 sub-decision A와 B를 포함하고 있으며, 각각 대체 시스템의 존재 여부에 따른 판단된 고장 level과 제어권 전환 여부에 따른 고장 단계를 판단한다. 제안된 판단 알고리즘은 종방향 고장 탐지 알고리즘을 기반으로 Matlab/Simulink 환경에서 3차원 차량 동역학 모델을 이용하여 성능평가를 수행하였다. This paper presents a fault level decision algorithm for functional safety of autonomous vehicles and longitudinal fault detection based performance evaluation. The fail-safe system of autonomous driving presented this study is divided into perception, decision and control steps for the failure. Each step detects and classifies the failure, decides the fault level, and suggests countermeasures according to the decision. In this study, fault level is defined by dividing faults into four levels, and a decision algorithm for resonable countermeasures for faults is presented. The fault decision algorithm includes sub-decisions A and B, where A is the level of decision about whether the system is in the substitute system, and B is the level of decision about whether the driver's ceding control is possible. Based on the fault detection algorithm, the proposed decision algorithm is evaluated using a 3D vehicle model in Matlab/Simulink environment.OAIID:RECH_ACHV_DSTSH_NO:T201900319RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A076898CITE_RATE:0DEPT_NM:기계항공공학부EMAIL:[email protected]_YN:YN

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

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%.N

Sliding Mode Observer based Fault Detection and Isolation Algorithm of Sensor for Longitudinal Autonomous Driving Using V2V Communication

본 연구는 차간 통신을 이용한 슬라이딩 모드 관측기 기반 종방향 자율주행 센서의 고장 탐지 및 분리 알고리즘에 관한 것이다. 고장탐지를 위해 차간 통신 정보 기반 획득된 선행차량의 종방향 가속도와 상대거리 및 속도 정보를 이용해 슬라이딩 모드 관측기 기반 자차량의 가속도 고장신호를 재건한다. 재건된 가속도 고장신호는 가속도 신호 및 환경센서 고장탐지에 사용되었다. 환경센서의 고장탐지를 위해 운동학 모델 기반 예측 고장탐지 알고리즘이 적용되었고, 재건된 가속도 고장신호에 고장탐지를 위한 경계값이 적용되었다. 제안된 고장탐지 및 분리 알고리즘의 합리적 성능평가를 위해 매틀랩/시뮬링크 환경에서 구성된 3차원 차량 동역학 모델과 실 주행정보가 사용되었으며, 선행차량 추종 제어 알고리즘이 적용되었다. 성능평가를 위한 고장신호로는 사각파와 삼각파와 같은 다양한 신호들이 적용되었다. This paper describes a sliding-mode-observer-based fault detection and isolation algorithm of sensor for longitudinal autonomous driving using vehicle-to-vehicle communication. The acceleration fault signal has been reconstructed based on the sliding-mode-observer using the acceleration from vehicle-to-vehicle communication, and the relative velocity and displacement values between preceding and subject vehicle. The reconstructed fault signal of acceleration has been used for fault detection of the environment and acceleration sensor. The kinematic-model-based predictive fault detection algorithm has been used for fault detection of the environment sensor. The threshold-value-based fault detection algorithm has been used for acceleration fault detection. For reasonable performance evaluation of the proposed fault detection isolation algorithm, actual driving data and a 3D vehicle dynamic model constructed in Matlab/Simulink environment have been used with square and triangular-wave fault signals.OAIID:RECH_ACHV_DSTSH_NO:T201912062RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A076898CITE_RATE:0DEPT_NM:기계항공공학부EMAIL:[email protected]_YN:YN

Development of driving-characteristic-analysis and driver-classification algorithms for ceding control of an autonomous vehicle

본 연구는 자율주행 자동차의 제어권 전환을 위한 주행 특성 분석 및 운전자 구분 알고리즘에 관한 연구이다. 자율주행 자동차는 고장 상태에 따라 제어권 전환이 선택적 또는 필수적으로 이루어져야 하며, 운전자에게 제어권이 전환되는 과정에서 전환에 대한 합리적 판단 기준은 고장-안전 시스템의 최적화를 위해 필수이다. 본 연구에서는 합리적 제어권 전환 판단을 위해 필요한 실 주행 데이터 기반 종방향 대표 주행 특성을 분석 및 도출하고, 가속도 - 역 충돌시간 평면에서 확률적으로 운전자를 구분하는 알고리즘을 제안하였다. 제안된 대표 주행 특성 및 운전자 구분 알고리즘의 성능평가를 위해 실 운전자 주행 데이터가 사용되었다. 성능평가 결과 본 연구에서 제안한 대표 주행 특성과 운전자 구분 알고리즘은 개별 운전자의 주행 특성을 나타내고, 합리적으로 구분할 수 있음을 확인하였다. This paper presents driving-characteristic-analysis and driver-classification algorithms for ceding control of an autonomous vehicle. Ceding control during autonomous driving must be conducted selectively or necessarily according to a fault condition, and the decision for ceding control of the autonomous vehicle should be conducted for optimizing the fail-safe system. To secure a reasonable decision for ceding control of an autonomous vehicle, actual driving data has been analyzed to determine the safety characteristics. An acceleration-inverse-time-to-collision plane-based probabilistic driver-classification algorithm has been proposed in this study. Various actual driving data have been used to conduct a reasonable performance evaluation of the analysis and classification algorithms. The results of the performance evaluation show that the algorithm can represent the driver's driving characteristics and reasonably classify the driver.OAIID:RECH_ACHV_DSTSH_NO:T201905260RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A076898CITE_RATE:0DEPT_NM:기계항공공학부EMAIL:[email protected]_YN:YN

Pancreatic/peripancreatic neurogenic tumor; little known masses not to be missed

Background: Retroperitoneal neurogenic tumors are extremely rare pathological entities; therefore, few clinical features and natural courses, especially originating from the pancreatic/peripancreatic regions, have been reported. This study aimed to investigate the clinicopathological features of pancreatic and peripancreatic neurogenic tumors and assess the diagnostic value of computed tomography (CT) and endoscopic ultrasound-guided fine needle biopsy (EUS-FNB). Methods: Between 2006 and 2018, patients who were diagnosed with neurogenic tumors were included. In total, 90 histologically confirmed cases of neurogenic tumors located in the pancreatic/peripancreatic regions were selected for analysis. Results: The mean age was 49.2 ± 13.1 years. There were no differences in sex distribution of the tumors. Schwannomas (44.4%) and paragangliomas (41.1%) were the most common neurogenic tumors. The sensitivity of CT was 62.2% in 90 cases. EUS-FNB was performed in 30 cases and the sensitivity of it was 83.3%. The diagnosis of neurogenic tumors with EUS-FNB or CT was not significantly associated with tumor location and size. Surgical resection was performed in 78 cases. Of the 12 patients who did not undergo surgery, 10 cases were followed-up without any increase in tumor size. Conclusions: Through the present study, we verified radiological, pathological, and clinical aspects of the pancreatic/peripancreatic neurogenic tumors which little known before, therefore, this study can serve as the basis for research to present an optimal diagnosis and treatment of neurogenic tumors. In addition, EUS-FNB is useful in the diagnosis of pancreatic/peripancreatic neurogenic tumors with relatively high sensitivity and can help establish therapeutic plans before the surgery