期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 62, 期 7, 页码 2906-2918出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2013.2260190
关键词
Nonlinear parameter identification; recursive least squares (RLS); tire-road friction estimation; vehicle dynamics
资金
- National Research Foundation of Korea
- Korean government (Ministry of Education, Science, and Technology) [2012-0000991]
- Ministry of Science, ICT and Future Planning, Korea, through the Convergence Information Technology Research Center support program [NIPA-2013-H0401-13-1008]
- National Research Foundation of Korea [2010-0028680] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split-mu surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.
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