A two-stage adaptive identification framework of the tire road friction coefficient considering the effect of multiple unknown measurement noises

• Autorzy: Zhang Fengjiao , Zhang Bo , Zhang Lanchun , Meng Ting , Wang Yan

Identyfikatory

DOI

10.24425/mms.2025.154334

• Języki publikacji: EN

Abstrakty

EN

The tire-road friction coefficient (TRFC) directly determines the available traction and braking forces of the tires, which in turn has a significant impact on vehicle stability control, particularly for commercial vehicles such as heavy-duty trucks. However, onboard sensors typically cannot directly measure the exact TRFC. To obtain an accurate TRFC, estimation algorithms are used, which rely on data from onboard sensors combined with vehicle and tire models. Since the signals required for estimation come from various types of sensors, in practice accurately obtaining the noise statistical characteristics of all sensors is highly challenging. Additionally, due to the complex and variable nature of vehicle operating conditions, noise tends to be time-varying as a result of environmental factors, which inevitably affects the accuracy of the estimation. To address these problems, we propose a two-stage adaptive identification framework that combines the extended H-infinity Kalman filter (EHKF) with the adaptive unscented Kalman filter (AUKF). First, in situations where the noise statistical characteristics are unknown, EHKF and the tire model are used to accurately estimate forces on the front and rear axles. Second, considering the time-varying nature of the noise, the AUKF, along with the vehicle model and axial force information, is employed to estimate the TRFC for the front and rear wheels. Finally, simulation tests on various road surfaces demonstrate that the two-stage adaptive identification method outperforms the unscented Kalman filter in terms of accuracy and stability.

Słowa kluczowe

EN

tire-road friction coefficient estimation; adaptive identification framework; extended H-infinity Kalman filter; adaptive unscented Kalman filter

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2025
• Tom: Vol. 32, nr 2
• Strony: 1--18
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr., wzory

Twórcy

autor

Zhang Fengjiao

• Changzhou Vocational Institute of Mechatronic Technology, College of Transportation Engineering, 26 Mingxin Middle Road, 213164 Changzhou, China

autor

Zhang Bo

• Changzhou Vocational Institute of Mechatronic Technology, College of Transportation Engineering, 26 Mingxin Middle Road, 213164 Changzhou, China

autor

Zhang Lanchun

• Jiangsu University of Technology, School of Automotive and Traffic Engineering, 1801 Zhongwu Road, 213001 Changzhou, China

autor

Meng Ting

• The Hong Kong Polytechnic University, School of Electrical and Electronic Engineering, 11 Kowloon Hung Hom Yuk Choi Road, 999077 Hong Kong, China

autor

Wang Yan

• The Hong Kong Polytechnic University, The Department of Industrial and Systems Engineering, 11 Kowloon Hung Hom Yuk Choi Road, 999077 Hong Kong, China

• https://orcid.org/0000-0001-5664-3994

Bibliografia

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Uwagi

This work was supported by the National Natural Science Funds for under Grant 52402482, in part sponsored by the QingLan Project of Jiangsu Higher Education Institutions (JSQL2022 and 2023), and in part by R&D Projects from Industry under Grant P0046083, P0048792.