A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics

IF 18.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-09-01 Epub Date: 2026-10-01 DOI:10.1109/JAS.2025.125366
Zhihong Man;Mingcong Deng;Zenghui Wang;Qing-Long Han
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Abstract

In this paper, the lateral dynamics of road vehicles (LDRV) is further studied from the viewpoint of vehicle informatics. It is seen that LDRV is first decoupled and the vehicle slip angle is proved to be observable from the yaw rate measurements. A new methodology of parameter estimation using steady-state yaw rate measurements (PESYRM) is then developed to accurately estimate the parameters of LDRV. The important characteristics of PESYRM comprise four parts: 1) The steering angle input to LDRV is chosen as the linear combination of sinusoids; 2) Only the steady state information of yaw rate in any fundamental period is required to accurately estimate the unknown parameters of LDRV; 3) Unlike many existing parameter estimation methods, the time consuming computing of the inverse of high-dimensional data matrix is avoided by making full use of the orthogonal properties of trigonometric base functions; 4) All of system information of LDRV is embedded in the measurements of the steady state yaw rate in any fundamental period. A simulation example is carried out to show the advantages and effectiveness of the new research findings for LDRV.
基于稳态横摆角速度测量的车辆横向动力学参数估计新方法
本文从车辆信息学的角度进一步研究了道路车辆的横向动力学问题。结果表明,LDRV首先解耦,并且从横摆角速度测量中可以观察到飞行器的滑移角。为了准确估计LDRV的参数,提出了一种基于稳态偏航率测量(PESYRM)的参数估计方法。PESYRM的重要特性包括四个部分:1)LDRV的转向角输入选择为正弦波的线性组合;2)准确估计LDRV的未知参数只需要任意基本时段的横摆角速度稳态信息;3)与现有的许多参数估计方法不同,充分利用三角基函数的正交特性,避免了高维数据矩阵逆的耗时计算;4) LDRV的所有系统信息都嵌入到任意基本周期稳态横摆角速度的测量中。仿真算例表明了新研究成果在LDRV中的优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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