標題: Vehicle Full-State Estimation and Prediction System Using State Observers
作者: Hsu, Ling-Yuan
Chen, Tsung-Lin
機械工程學系
Department of Mechanical Engineering
關鍵字: Extended Kalman filtering (EKF);road angles;rollover prediction;state estimation;state observers;state prediction;switching computation scheme
公開日期: 1-七月-2009
摘要: This paper presents a novel vehicle full-state estimation and prediction system that employs a "full-state vehicle model" together with lateral acceleration, longitudinal velocity, and suspension displacement sensors to obtain the current and future vehicle state information. The full-state vehicle model is a vehicle model with 6 degrees of freedom (DOFs) and is described by 20-state nonlinear differential equations. The proposed approach differs from those in most of the existing literatures in three aspects. First, the road angles and the nonlinear suspension systems are incorporated into the vehicle modeling. Second, the "switching observer scheme" is introduced to significantly reduce the heavy work load that is required for the mathematical derivations. Finally, the full-state vehicle model is employed to predict the vehicle dynamics at future times. The simulation results show that the proposed system can accurately estimate and predict the state values. The relative accuracy of the state estimation is 2.66% on average and 2.86% on average of the state prediction. Furthermore, the proposed system can predict whether the vehicle rollover will occur when a vehicle performs a quick turn on a slope road.
URI: http://dx.doi.org/10.1109/TVT.2008.2008811
http://hdl.handle.net/11536/7006
ISSN: 0018-9545
DOI: 10.1109/TVT.2008.2008811
期刊: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume: 58
Issue: 6
起始頁: 2651
結束頁: 2662
顯示於類別:期刊論文


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