Sideslip angle estimation using extended Kalman filter
Sideslip angle estimation using extended Kalman filter
复制标题
DOI：
10.1080/00423110801958550
复制
发表时间：
2008-01-01
影响因子：
3.6
通讯作者：
Hsieh, F-C.
中科院分区：
文献类型：
--
作者：
Chen, B. -C.;Hsieh, F-C.
车辆侧滑角可以使用动力学或运动学模型来估计。动态模型需要车辆参数，这些参数可能由于不同的负载条件而具有不确定性。车辆运动和道路摩擦。参数不确定性可能导致估计误差。因此，需要系统辨识来在线估计这些参数。另一方面，运动学模型不需要这些参数。闭环估计器可以用公式表示，使用运动学模型估计侧滑角。由于由横摆率组成的系统矩阵是时变的。所需的输入矢量和输出包含过程和测量噪声。分别干扰输入矩阵中包含状态估计值，采用扩展卡尔曼滤波器获得估计增益，在Matlab/Simulink中利用CarSim对不同驾驶场景和路面摩擦情况下所提出的方法进行仿真。初步结果表明，有前途的改善侧滑角估计。
Vehicle sideslip angle can be estimated using either dynamic or kinematic models. The dynamic model requires vehicle parameters, which might have uncertainties due to different load conditions. vehicle motions, and road frictions. parameter uncertainties might result in estimation errors. Thus system identifications are required to estimate those parameters online. On the other hand, the kinematic model does not require these parameters. A closed-loop estimator can be formulated 10 estimate the sideslip angle using the kinematic model. Since the system matrix which consists of the yaw rate is time varying. the required input Vector and output contain process and measurement noises. respectively. and the disturbance input matrix contains estimated states, extended Kalman filter is used to obtain the estimation gain in this paper CarSim is used to evaluate the proposed approach under different driving scenarios and road frictions in Matlab/Simulink. The preliminary results show promising improvement of the sideslip angle estimation.