Modeling and Identification of Passenger Car Dynamics Using Robotics Formalism

Abstract

Abstract-This paper deals with the problem of dynamic modeling and identification of passenger cars. It presents a new method that is based on robotics techniques for modeling and description of tree-structured multibody systems. This method enables us to systematically obtain the dynamic identification model, which is linear with respect to the dynamic parameters. The estimation of the parameters is carried out using a weighted least squares method. The identification is tested using vehicle dynamics simulation software used by the car manufacturer PSA Peugeot-Citroën in order to define a set of trajectories with good excitation properties and to determine the number of degrees of freedom of the model. The method has then been used to estimate the dynamic parameters of an experimental Peugeot 406, which is equipped with different position, velocity, and force sensors.

Real-time identification and visualization of human segment parameters

“…The colors are chosen according to the relative standard deviation calculated for each base parameter. [15], [17] The covariance matrix C n ∈ R NB×NB of the estimation error ofφ B,n are computed as follow: (15) where E is the expectation operator. Eq.…”

Monitoring the segment parameters during long term physical training from motion capture data

“…Using the linearity in the parameters to estimate the identification model, it is common to use a vertical concatenation of M different prescribed motions that each excites specific dynamics to create the regressor [9], [10], as shown in Eq. 4.…”

“…The actual excitation properties of the motion are verified a-posteriori by looking at the condition number of each individual regressor and at the resulting regressor [10]. However with complex systems it is difficult to interpret directly the obtained values of the condition number of the regressor and to understand the actual excitation properties in detail as two regressors, obtained with two different motions, can have the same condition number as it will be detailed in section IV.…”

Motion capture based identification of the human body inertial parameters

“…As shown in [8] and [13], the inverse dynamics can be expressed in a linear form with respect to the dynamic parameters. Thus, by separating the vector of inertial parameters φ from the observation matrix Y the identification model is obtained in Eq.…”