System identification of a dynamical model of a vehicle using data generated by a high-fidelity simulator

Abstract

Unmanned vehicles are a vital topic in today’s science and technology field. The safety problem of unmanned vehicles has been paid more attention from researchers. People are continually developing new control technologies, making the auxiliary driving or control of vehicles more accurate and reliable. Before designing a reliable controller, researchers need to obtain an accurate model of the vehicle system. However, the vehicle is a complex system, and various vehicle parameters are difficult to obtain by direct sensor measurement. In addition, there are deviations between the actual vehicle and the simulation model. At this time, it is necessary to make system identification to obtain reliable vehicle parameters and models. In this paper, vehicle simulation is carried out based on the CarSim C-Class model under the environment of CarSim vehicle simulation software. The model includes tire, suspension, steering, driver, and other subsystems. This platform can simulate a vehicle closed to the real one. The vehicle model can be controlled through Simulink and output the real-time data of various variables of the vehicle system. Then, the data required for identification and validation can be obtained through the joint simulation of CarSim and Simulink. By comparing different vehicle and tire models, different identification data sets and different algorithms, this paper summarizes the advantages and disadvantages of different choices and their applicability. First, the comparison between the bicycle model and the four wheels vehicle model is implemented. The Interior-point algorithm was used to identify the two models under different control data sets. The results of parameter validation and vehicle validation are analyzed. Then, under the same control data set, the four wheels model is selected for the comparative experiment between different algorithms. The first comparison with the Interior-point algorithm is the Unscented Kalman Filter (UKF) and its improved method Particle Swarm Optimization-Unscented Kalman Filter (PSO-UKF). The Magic Formula tire model was then identified by Genetic Algorithm (GA) algorithm, which compares with the Dugoff tire model. Each model and algorithm has its suitable scenarios. Also different data sets lead to various result. The analysis and application suggestions of different algorithms, data sets and models will be given in the end.