A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient

IF 1 4区工程技术 Q4 ENGINEERING, MECHANICAL

Mechanical Sciences Pub Date : 2022-08-25 DOI:10.5194/ms-13-735-2022

Gengxin Qi, Xiao-bin Fan, Hao Li

{"title":"A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient","authors":"Gengxin Qi, Xiao-bin Fan, Hao Li","doi":"10.5194/ms-13-735-2022","DOIUrl":null,"url":null,"abstract":"Abstract. The measurement of the road adhesion coefficient is of great\nsignificance for the vehicle active safety control system and is one of the key\ntechnologies for future autonomous driving. With a focus on the problems of\ninterference uncertainty and system nonlinearity in the estimation of the road\nadhesion coefficient, this work adopts a vehicle\nmodel with 7 degrees of freedom (7-DOF) and the Dugoff tire model and uses these models to estimate the road adhesion\ncoefficient in real time based on the particle filter (PF) algorithm. The\nestimations using the PF algorithm are verified by selecting typical\nworking conditions, and they are compared with estimations using the unscented\nKalman filter (UKF) algorithm. Simulation results show that the road adhesion\ncoefficient estimator error based on the UKF algorithm is less than 7 %, whereas the road adhesion coefficient estimator error based on the PF algorithm is\nless than 0.1 %. Thus, compared with the UKF algorithm, the\nPF algorithm has a higher accuracy and control effect with respect to\nestimating the road adhesion coefficient under different road conditions. In order to\nverify the robustness of the road adhesion coefficient estimator, an\nautomobile test platform based on a four-wheel-hub-motor car is built.\nAccording to the experimental results, the estimator based on the PF algorithm\ncan realize the road surface identification with an error of less than 1 %,\nwhich verifies the feasibility and effectiveness of the algorithm with respect to\nestimating the road adhesion coefficient and shows good robustness.\n","PeriodicalId":18413,"journal":{"name":"Mechanical Sciences","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Sciences","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5194/ms-13-735-2022","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}

引用次数: 1

Abstract

Abstract. The measurement of the road adhesion coefficient is of great significance for the vehicle active safety control system and is one of the key technologies for future autonomous driving. With a focus on the problems of interference uncertainty and system nonlinearity in the estimation of the road adhesion coefficient, this work adopts a vehicle model with 7 degrees of freedom (7-DOF) and the Dugoff tire model and uses these models to estimate the road adhesion coefficient in real time based on the particle filter (PF) algorithm. The estimations using the PF algorithm are verified by selecting typical working conditions, and they are compared with estimations using the unscented Kalman filter (UKF) algorithm. Simulation results show that the road adhesion coefficient estimator error based on the UKF algorithm is less than 7 %, whereas the road adhesion coefficient estimator error based on the PF algorithm is less than 0.1 %. Thus, compared with the UKF algorithm, the PF algorithm has a higher accuracy and control effect with respect to estimating the road adhesion coefficient under different road conditions. In order to verify the robustness of the road adhesion coefficient estimator, an automobile test platform based on a four-wheel-hub-motor car is built. According to the experimental results, the estimator based on the PF algorithm can realize the road surface identification with an error of less than 1 %, which verifies the feasibility and effectiveness of the algorithm with respect to estimating the road adhesion coefficient and shows good robustness.

查看原文本刊更多论文

无迹卡尔曼滤波器和粒子滤波器估计道路附着系数方法的比较研究

摘要道路附着系数的测量对车辆主动安全控制系统具有重要意义，是未来自动驾驶的关键技术之一。针对路面附着系数估计中的干扰不确定性和系统非线性问题，本文采用7自由度车辆模型和Dugoff轮胎模型，并利用这些模型基于粒子滤波器算法实时估计路面附着系数。通过选择典型的工作条件，验证了PF算法的估计，并将其与无目标卡尔曼滤波器（UKF）算法的估计进行了比较。仿真结果表明，基于UKF算法的道路粘着系数估计误差小于7 %, 而基于PF算法的道路附着系数估计器误差小于0.1 %. 因此，与UKF算法相比，PF算法在估计不同路况下的道路附着系数方面具有更高的精度和控制效果。为了验证道路附着系数估计器的鲁棒性，建立了一个基于四轮毂汽车的自动移动测试平台。根据实验结果，基于PF算法的估计器可以实现误差小于1的路面识别 %,验证了算法在道路附着系数估计方面的可行性和有效性，并显示出良好的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Mechanical Sciences ENGINEERING, MECHANICAL-

CiteScore

2.20

自引率

7.10%

发文量

审稿时长

29 weeks

期刊介绍： The journal Mechanical Sciences (MS) is an international forum for the dissemination of original contributions in the field of theoretical and applied mechanics. Its main ambition is to provide a platform for young researchers to build up a portfolio of high-quality peer-reviewed journal articles. To this end we employ an open-access publication model with moderate page charges, aiming for fast publication and great citation opportunities. A large board of reputable editors makes this possible. The journal will also publish special issues dealing with the current state of the art and future research directions in mechanical sciences. While in-depth research articles are preferred, review articles and short communications will also be considered. We intend and believe to provide a means of publication which complements established journals in the field.