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다양한 주행 상황에 견실한 차량 롤 각 추정 로직 개발
조건희(Kunhee Cho),나호용(Hoyong Na),유승한(Seung-Han You) 대한기계학회 2019 大韓機械學會論文集A Vol.43 No.10
본 논문에서는 별도의 고가 센서 장착 없이 양산 차량에서 가용한 센서 정보만을 이용하여 롤 전복 상황 및 종/횡 경사로 주행과 같이 기존 추정 방법으로는 추정이 어려운 악의적 주행 조건에서도 강건한 롤 각 추정 로직을 개발하였다. 견실한 차량의 롤 각 추정을 위해 정적인 주행 상황에서는 횡 가속도, 요 레이트 센서 계측치를 주로 이용하여 롤 각을 추정하고, 횡 방향 동적 거동이 큰 상황에 대해서는 롤 레이트 적분에 가중치를 더 부여하여 롤 각을 추정하였다. 제안한 추정 알고리즘은 평지 주행상황뿐만 아니라 횡 경사로 주행, 주차타워 주행, 롤 전복 상황과 같은 다양한 실차 주행 시나리오들에서 검증되었다. 그 결과, 제안된 롤 각 추정 로직이 여러 악의 주행 조건들에서 추정 성능을 견실하게 유지하는 것을 확인하였다. This paper presents a vehicle roll angle estimator that is robust to challenging driving situations such as rollover and driving on longitudinal/lateral slopes without installing separate high-cost sensors. To estimate vehicle roll angle in static situation, the lateral acceleration and yaw rate are mainly used to calculate static roll angle. Moreover, the integration of roll rate is chiefly used when the vehicle transient lateral motion is quite large. This proposed algorithm is validated via vehicle experiments in various scenarios such as slalom on a flat road, driving on a banked road and parking tower, and rollover situation. The experimental results indicate that the proposed algorithm robustly maintains the accuracy of estimation in various driving situations.
자율주행 횡방향 제어를 위한 Gaussian Processes 기반의 차량 모델 개발
조건희(Kunhee Cho),전명환(Myungwhan Jeon),이형철(Hyeongcheol Lee) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
Various methods have been proposed for the development of lateral controller for autonomous vehicle over decades. Model predictive control (MPC), which utilize vehicle models and optimization methods considering future information and state constraints, are widely used for designing a lateral controller of autonomous vehicle. However, the vehicle models used in MPC typically employ linear models to reduce computational complexity, which results in prediction errors and a degradation in controller performance for highly nonlinear regions of the vehicle’s behavior. In this paper, we designed a gaussian process (GP)-based vehicle model to improve the prediction performance of lateral controllers for autonomous vehicle. First, we conducted signal measurements necessary for the model construction through real-world vehicle experiments and preprocessing for GP design. Especially, the lateral velocity, which is the output of the model, was measured via RTK/GNSS and utilized for optimizing the GP for the model construction. Through offline optimization process, we derived the types of kernels and hyperparameters of the GP to improve model accuracy. The proposed vehicle model was evaluated for various driving scenarios, such as lane changes and steady circular turns.
Reference Governor 기반의 ACC 시스템 설계
문일경(Ilkyoung Mun),조건희(Kunhee Cho),이형철(Hyeongcheol Lee) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper represents adaptive cruise control system based on reference governor. Reference governor is add-on control schemes which enforce state and control constraints on stabilized systems by modifying the reference whenever the constraints are violated. Unlike existing controllers that do not handle states and input constraints, the proposed method handles constraints using maximal admissible set to consider actuator limits, ride quality, vehicle safety, and fuel consumption with low computational effort. Simulation results are given to show the performance of the proposed method compared to LQT and MPC.
송은아(Euna Song),조건희(Kunhee Cho),이형철(Hyeongcheol Lee) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
Currently, wheel torque is calculated by receiving the estimated torque of the drive system controller via CAN. But, if there is a problem with the controllers calculation, the hazard of the vehicle cannot be detected, so a separate wheel torque estimation method is required for functional safety response. Therefore, this paper developed a wheel torque estimation algorithm to improve torque output fault diagnosis. The algorithm is based on vehicle dynamics (longitudinal model) and is constructed through a sliding mode observer (SMO), and the consistency of the developed algorithm is verified through vehicle dynamics simulation.