Abstract:

The vehicle dynamics modeling process based on mechanism analysis is usually simplified with assumptions，which can't accurately calculate the dynamic changes of actual vehicles under different road conditions， thus causing problems such as low trajectory tracking control accuracy and instability of intelligent automotive. To tackle the above-mentioned problems， this paper proposes a non-linear modeling and control method based on hybrid modeling technology. By constructing mechanism analysis - data-driven vehicle dynamics series hybrid model， the vehicle state and control data are calculated and processed by the mechanism model， and then used as the input of the data-driven module after a level combination. Besides， long-short-term memory network used as the backbone realizes the nonlinear correlation feature extraction of time-series data and the final model output calculation. The test results show that the model can supplement some unmodeled dynamics in the mechanism model， improve the model calculation accuracy and has the ability to implicitly understand different road adhesion conditions. In addition， the Euler integration is used to complete the discretization of the prediction model and design the model predictive control track tracking algorithm. The feedforward feedback control algorithm is designed to provide external input required by the prediction model in the horizontal control while realizing the longitudinal control of the vehicle， finally achieving more accurate trajectory tracking control effect that is more in line with the actual driving environment. The co-simulation results of Carsim / Simulink show that the method achieves accurate output of different road attachment coefficients， synchronously enhances the intelligent automotive trajectory tracking control accuracy and stability， and has good horizontal and longitudinal coordination control.

Key words: intelligent vehicle, trajectory tracking, data-driven modeling, model predictive control