基于交互式多模型无迹卡尔曼滤波的悬架系统状态估计

doi:10.3969/j.issn.1000-1093.2021.02.003

摘要/Abstract

摘要： 为有效解决复杂行驶工况下非线性悬架系统运动状态无法精确获取的难题，实现模型参数不确定以及时变路面激励工况下悬架状态精确估计的目标，开展了悬架系统状态估计研究。在路面激励模型和非线性悬架系统模型的基础上，结合交互式多模型算法与基于马尔可夫链的蒙特卡洛理论，设计了考虑模型参数不确定以及时变路面激励工况下多模型交互无迹卡尔曼滤波（IMMUKF）状态估计算法，且利用随机控制稳定判据验证了所设计的非线性观测器稳定性判定。对比分析了不同路面激励工况下悬架系统对于传统无迹卡尔曼滤波观测器与IMMUKF观测器的状态估计精度，并进行了台架试验验证。试验与仿真结果表明，IMMUKF观测器可获取更高的系统状态识别精度，不同路面激励仿真工况下状态估计误差最大均方根值不超过8%.

关键词: 悬架系统, 状态估计, 无迹卡尔曼滤波, 交互式多模型, 马尔可夫链, 蒙特卡罗

Abstract: The accuracy estimation of suspension state under the conditions of time-varying road excitation and model parameter uncertainty is realized to effectively solve the issue that the state estimation of the nonlinear suspension system cannot be accurately achieved under complex driving conditions. The state estimation of suspension system is studied. Based on the models of road profile excitation and nonlinear suspension system, a novel interacting multiple model unscented Kalman filter (IMMUKF) algorithm is designed using the interacting multiple model algorithm and Markovchain Monte Carlo theory. IMMUKF algorithm is used to estimate the movement state of suspension system under various working conditions. The stability conditions of the proposed algorithm is validated using the stochastic stability theory. The accuracy of the nonlinear suspension movement state was estimated in real-time by comparing the traditional unscented Kalman filter (UKF) algorithm with the proposed IMMUKF algorithm under the various road inputs, and the suspension system was tested and verified. Experimental and simulated results show that the higher accuracy of the proposed algorithm can be obtained, and the maximum root mean square error of state estimation of the proposed algorithm in simulation is less than 8%.

Key words: suspensionsystem, stateestimation, interactingmultiplemodel, unscentedKalmanfilter, Markovchainmatrix, MonteCarlo

中图分类号:

王振峰，李飞，王新宇，杨建森，秦也辰. 基于交互式多模型无迹卡尔曼滤波的悬架系统状态估计[J]. 兵工学报, 2021, 42(2): 242-253.

WANG Zhenfeng， LI Fei， WANG Xinyu， YANG Jiansen， QIN Yechen. State Estimation of Suspension System Based on Interacting Multiple Model Unscented Kalman Filter[J]. Acta Armamentarii, 2021, 42(2): 242-253.

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