Real-Time Estimation of Center of Gravity Position for Lightweight Vehicles Using Combined AKF-EKF Method

被引:57
作者
Huang, Xiaoyu [1 ]
Wang, Junmin [1 ]
机构
[1] Ohio State Univ, Dept Mech & Aerosp Engn, Columbus, OH 43210 USA
基金
美国国家科学基金会;
关键词
Adaptive Kalman filter (AKF); center of gravity (CG); extended Kalman filter (EKF); lightweight vehicle (LWV); optimization; parameter estimation; road grade; VELOCITY ESTIMATION; STATE ESTIMATION; ROAD GRADE; PARAMETER; DESIGN; OBSERVER;
D O I
10.1109/TVT.2014.2312195
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a real-time center of gravity (CG) position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
引用
收藏
页码:4221 / 4231
页数:11
相关论文
共 35 条
[1]   Design and Evaluation on Electric Differentials for Overactuated Electric Ground Vehicles With Four Independent In-Wheel Motors [J].
Chen, Yan ;
Wang, Junmin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2012, 61 (04) :1534-1542
[2]   Adaptive Vehicle Speed Control With Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation [J].
Chen, Yan ;
Wang, Junmin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2011, 60 (03) :839-848
[3]   Parameter and state estimation for articulated heavy vehicles [J].
Cheng, Caizhen ;
Cebon, David .
VEHICLE SYSTEM DYNAMICS, 2011, 49 (1-2) :399-418
[4]   Slip-based tire road friction estimation [J].
Gustafsson, F .
AUTOMATICA, 1997, 33 (06) :1087-1099
[5]  
Huang J., 2008, Proceedings of the 2008 ASME Dynamic Systems and Control Conference, P103, DOI DOI 10.1115/DSCC2008
[6]   Center of gravity height real-time estimation for lightweight vehicles using tire instant effective radius [J].
Huang, Xiaoyu ;
Wang, Junmin .
CONTROL ENGINEERING PRACTICE, 2013, 21 (04) :370-380
[7]   Model predictive regenerative braking control for lightweight electric vehicles with in-wheel motors [J].
Huang, Xiaoyu ;
Wang, Junmin .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2012, 226 (D9) :1220-1232
[8]   Lightweight Vehicle Control-Oriented Modeling and Payload Parameter Sensitivity Analysis [J].
Huang, Xiaoyu ;
Wang, Junmin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2011, 60 (05) :1999-2011
[9]   Vehicle velocity estimation using nonlinear observers [J].
Imsland, Lars ;
Johansen, Tor A. ;
Fossen, Thor I. ;
Grip, Havard Fjaer ;
Kalkkuhl, Jens C. ;
Suissa, Avshalom .
AUTOMATICA, 2006, 42 (12) :2091-2103
[10]  
Jazar R.N., 2008, VEHICLE DYNAMICS THE, P114