悬挂(拓扑)
卡尔曼滤波器
磁流变液
控制理论(社会学)
计算机科学
地形
国家(计算机科学)
工程类
控制工程
数学
人工智能
控制(管理)
地理
算法
阻尼器
地图学
纯数学
同伦
作者
Weihua Li,Ling Chen,Jie Fu,Lei Luo,Miao Yu
标识
DOI:10.1088/1361-665x/ad9441
摘要
Abstract For the magnetorheological (MR) suspension control system of all-terrain vehicles (ATVs), state estimation is an effective method to obtain system feedback signals that cannot be directly measured by sensors. However, when confronted with modeling errors and sudden changes in sensor noise during complex road driving, conventional estimation methods with fixed parameters encounter challenges in accurately estimating the states of ATV suspension system. To address this issue, this paper introduces a novel adaptive Sage-Husa Kalman filter (ASHKF) algorithm to estimate the sprung and unsprung velocity of ATV suspension system. The algorithm uses exponential weighting function and gradient detection function to adaptively adjust the attenuation coefficient according to the driving conditions of the ATV, thereby realizing real-time correction of the covariance matrix of the prediction error. Ultimately, through simulation and real-vehicle testing, it is demonstrated that the designed ASHKF is able to effectively improve the state estimation accuracy of the speed signal of the suspension system under off-road driving conditions with low-frequency noise and outlying disturbances, and the accuracy is improved by 62.70% compared with that of the conventional Sage-Husa Kalman filter (SHKF).
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