|Low rate error Modeling of Articulated Heavy Vehicle Dynamics and Experimental Validation
Mohamed Bouteldja* and Veronique Cerezo
International Journal of Control, Automation, and Systems, vol. 15, no. 5, pp.2203-2212, 2017
Abstract : "In the automotive domain, adequate control and diagnosis rely on the use of state observers and parametric
identification systems to estimate the dynamics performances of the vehicle. Unfortunately, the simultaneous
use of different methods of observation, estimation and identification is not risk-free. The risks can be expressed
mathematically through a problem of error accumulation, posing major risks for the vehicle and its driver (errors
of detection, errors in the prediction of dangerous driving situations, vehicle instability, etc.). This paper presents
a method of observation and estimation of the dynamic state and parameter identification of an articulated vehicle
simultaneously at very low error rates. This method is based on the HOSM (High Order Sliding Modes) approach,
with the application of the STA (Super-Twisting Algorithm). Towards to this aim, a 5-DOF (Degree Of Freedom)
nonlinear dynamic model for an articulated vehicle is proposed. The model is derived by applying Lagrange’s equations.
Simulation and experimental results showed that the algorithms generate accurate estimation of articulated
vehicle parameters and states dynamics in real driving situations."
"Estimation, experimental validation, heavy vehicle, identification, modelling, second order sliding mode observer."