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Estimation of Vehicle Sideslip Angle Based-on Unscented Kalman Filter

机译:基于Unscented Kalman滤波器的车辆侧线角度估计

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The sideslip angle of the center of mass is one of the important control variables in the steering stability control system of forklift, and it is also difficult to measure directly. According to the characteristics of the sideslip angle which is not easy to measure, the paper takes the four wheel steering forklift of the front wheel and rear wheel active control as the research object. Based on the nonlinear two degree of freedom (2DOF) forklift dynamics model and the driver-forklift closed-loop system model, and based on extended Kalman filter (EKF) and unscented Kalman filter (UKF), the sideslip angle estimation model is established, and the steps of UKF algorithm are given in detail. Simulation results show that the estimation accuracy of UKF algorithm is better than that of EKF algorithm in either open-loop or closed-loop systems, it can better meet the state estimation requirements of forklift.
机译:质心的侧滑角是转向稳定性控制系统中的重要控制变量之一,并且也难以直接测量。根据不容易测量的侧滑角的特点,纸张采用前轮和后轮主动控制的四轮转向叉车作为研究对象。基于非线性两度自由度(2DOF)叉车动力学模型和驾驶员叉车闭环系统模型,基于扩展卡尔曼滤波器(EKF)和Unscented Kalman滤波器(UKF),建立了侧滑角估计模型,并且详细给出了UKF算法的步骤。仿真结果表明,UKF算法的估计精度优于开环或闭环系统中EKF算法的估计精度,可以更好地满足叉车的状态估计要求。

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