It is well recognized in the automotive research community that knowledge of the real-time tyre-road friction conditions can be extremely valuable for intelligent safety applications, including design of braking, traction, and stability control systems. This paper presents a new development of an on-line tyre-road adherence estimation methodology and its implementation using both Burckhardt and LuGre tyre-road friction models. The proposed strategy first employs the recursive least squares to identify the linear parameterization (LP) form of Burckhardt model. The identified parameters provide through a Takagi-Sugeno (T-S) fuzzy system the initial values for the LuGre model. Then, it is presented a new large-scale optimization based estimation algorithm using the steady state solution of the partial differential equation (PDE) form of LuGre to obtain its parameters. Finally, real-time simulations in various conditions are provided to demonstrate the efficacy of the algorithm.
Tyre-Road Adherence Conditions Estimation for Intelligent Vehicle Safety Applications / Sharifzadeh, Mojtaba; Timpone, Francesco; Farnam, Arash; Senatore, Adolfo; Akbari, Ahmad. - 47:(2017), pp. 389-398. (Intervento presentato al convegno The First International Conference of IFToMM Italy (IFIT 2016) tenutosi a Department of Management and Engineering (DTG), University of Padova, Vicenza, Italy nel December 1-2, 2016) [10.1007/978-3-319-48375-7_42].
Tyre-Road Adherence Conditions Estimation for Intelligent Vehicle Safety Applications
TIMPONE, FRANCESCO;
2017
Abstract
It is well recognized in the automotive research community that knowledge of the real-time tyre-road friction conditions can be extremely valuable for intelligent safety applications, including design of braking, traction, and stability control systems. This paper presents a new development of an on-line tyre-road adherence estimation methodology and its implementation using both Burckhardt and LuGre tyre-road friction models. The proposed strategy first employs the recursive least squares to identify the linear parameterization (LP) form of Burckhardt model. The identified parameters provide through a Takagi-Sugeno (T-S) fuzzy system the initial values for the LuGre model. Then, it is presented a new large-scale optimization based estimation algorithm using the steady state solution of the partial differential equation (PDE) form of LuGre to obtain its parameters. Finally, real-time simulations in various conditions are provided to demonstrate the efficacy of the algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
