Vehicle handling dynamics modelling by a data-driven identification method

Abstract

Modelling of vehicle handling dynamics has received a renewed attention in recent years. Different from traditional vehicle modelling, a novel data-driven identification method for vehicle handling dynamics is proposed, which can avoid the problems of the under-modelling and parameter uncertainties in the first-principle modelling process. By first-order Taylor expansion, the nonlinear vehicle system can be linearised as a slowly linear time-varying system with fourth-order. In order to identify the derived identifiable model structure, a recursive subspace method is presented. Derived by optimal version of predictor-based subspace identification (PBSID opt ) and projection approximation subspace tracking (PAST), the identification method is numerical stability and gives an unbiased estimation for the closed-loop system. Based on standard road tests, the proposed modelling method is proven effective and the obtained model has good predictive ability. Additionally, it is noted that the model obtained from the initial phase of straight driving is just a mathematical model to describe the relationship between input and output. And when the vehicle is steering, the model can converge to a stable phase quickly and represent vehicle dynamic performance.

A feedback linearization controller combined with a data-driven subspace-based prediction method for vehicle handling stabilization

“…Combining PBSID method with PAST function, Guan et al. 27 and Ba et al. 28 applied this recursive identification scheme to vehicle handling dynamics modeling and parameters estimation.…”

“…For real-time observability matrix updating, Gustafsson 26 discussed a projection approximation subspace tracking (PAST) function. Combining PBSID method with PAST function, Guan et al 27 and Ba et al 28 applied this recursive identification scheme to vehicle handling dynamics modeling and parameters estimation. In spite of obtaining an acceptable estimation result, this SIM still suffers from less accuracy due to the approximated unconstrained cost function involved in identification process.…”

A comprehensive active-steering control method for improvement of vehicle handling performance

“…While developing control strategies, the majority of researchers take yaw motion of the two-degrees-of freedom (2-DOF) reference model as the desired vehicle behaviour. [10][11][12][13][14] Traditional vehicle stabilizing methods suffer from the non-linear characteristics in the controller designing and modelling. 15 Although some studies take vehicle parameter uncertainty and non-linearity into consideration, [16][17][18][19] validity of these approximately linearized controllers is limited into a certain scope.…”

“…While developing control strategies, the majority of researchers take yaw motion of the two-degrees-of freedom (2-DOF) reference model as the desired vehicle behaviour. 10–14…”

Kalman and particle filtering methods for full vehicle and tyre identification

“…In recent years, dynamic modelling has also become increasingly important in vehicle development, due to obvious time and cost saving advantages over traditional prototype testing [1]. Regardless of the application, a compromise between accuracy and complexity needs to be sought, as the ability to run in real time is a major goal in both control and simulation.…”