A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States.
A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States.
复制标题
用于估计与时间无关/与时间相关的参数和状态的融合算法
DOI：
10.3390/s21124068
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发表时间：
2021-06-12
期刊：
影响因子：
--
通讯作者：
Qi H
中科院分区：
文献类型：
--
作者：
Zhang Z;Zhang J;Dai J;Zhang B;Qi H
车辆参数对于动力学分析和控制系统是必不可少的。现有的车辆参数估计算法存在的一个问题是：实时估计方法只能识别部分车辆参数，而其他参数如悬架阻尼系数、悬架刚度和轮胎刚度等都是通过惯性参数测量装置（IPMD）预先已知的。在这项研究中，提出了一种融合算法，用于识别综合车辆参数没有IPMD的帮助下，和车辆参数分为时间无关的参数（TIPs）和时间相关的参数（TDPs）的基础上，他们是否随时间变化。TIP由混合质量状态变量（HMSV）识别。一个双无迹卡尔曼滤波器（DUKF）被应用到更新TDPs和在线状态。在一辆真实的两轴车辆上进行了试验，并利用试验数据估计了TIPs和TDPs，验证了该算法的准确性。通过数值仿真进一步研究了该算法在簧载质量变化、线性化模型误差和各种路面条件下的性能。实验和仿真结果表明，该算法在不需要道路信息的情况下，能够准确地估计出TIPs，更新TDPs和在线状态，具有较高的精度和较快的收敛速度。
Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.
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