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RISS 인기검색어
- 검색키워드
- 저자 : Jingan Feng (검색결과 3 건)
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Zhang Feng,Feng Jingan,Qi Dengliang,Liu Ya,Shao Wenping,Qi Jiaao,Lin Yuangang 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.6
To address the problem of poor robustness and accuracy of vehicle state and parameter estimation by conventional Kalman filter in the non-Gaussian environments, a three-degree-of-freedom vehicle model with an improved Dugoff tire model is established and a joint estimator of vehicle state and parameter is designed using the Maximum Correntropy (MC) adaptive unscented Kalman filter (AUKF) algorithm in order to simultaneously estimate and identify the yaw rate, longitudinal vehicle speed, lateral vehicle speed, vehicle mass and rotational inertia. The proposed joint estimator algorithm was validated by Simulink/CarSim simulation testbed under Double Lane Change and Sine Wave Steering Input conditions. The results show that MC combined with AUKF (MCAUKF) algorithm has higher estimation accuracy and better convergence compared to the unscented Kalman filter (UKF) and the MC combined with UKF (MCUKF) in non-Gaussian environments, and the MCAUKF estimator is more suitable for state estimation and parameter identification of real vehicles.
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MAXIMUM CORRENTROPY EXTENDED KALMAN FILTER FOR VEHICLE STATE OBSERVATION
Qi Dengliang,Feng Jingan,Ni Xiangdong,Wang Lei 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.2
For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations.
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Design of vortex finder structure for decreasing the pressure drop of a cyclone separator
Yaquan Sun,Junzhi Yu,Weibing Wang,Shanglin Yang,Xue Hu,Jingan Feng 한국화학공학회 2020 Korean Journal of Chemical Engineering Vol.37 No.5
The structure of the vortex finder has an important influence on the pressure drop and separation efficiency of a cyclone, which mainly governs the separation process. In this paper, the traditional vortex finder is slotted on side wall and its bottom is closed, i.e., a slotted vortex finder. The impact of slotted vortex finder on the separation performance of a cyclone is explored by using numerical simulation and experimental validation. Specifically, the gas phase is studied by the Reynolds stress model (RSM), and the particle phase is simulated by the discrete phase model (DPM). The simulation results are in good agreement with the experimental results, revealing higher prediction accuracy. The results indicate that the slotted vortex finder can effectively suppress the generation of the downward swirling flow at the center of the vortex finder and decrease the turbulence intensity at the bottom of the vortex finder and the outer vortex, thereby decreasing the energy loss and increasing the separation efficiency. When the slot length is 0.2De, the slotted vortex finder can reduce the pressure drop by 143.33 Pa while increasing the collection efficiency by 5.51%.
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