NEURAL NETWORK LATERAL DYNAMICS MODELING AND CONTROL BASED ON ED-LSTM FOR INTELLIGENT VEHICLE

Simplification and assumptions are usually made in the process of vehicle dynamics modeling, resulting in the model can not accurately reflect the actual dynamic characteristics of the vehicle under some working conditions, which affects the control accuracy and even safety. In view of this, this paper proposes a data-driven nonlinear modeling and control method, establishes a new neural network multi-step prediction model of vehicle lateral dynamics, and realizes the tracking control of reference trajectory of intelligent vehicle. Firstly, based on the analysis of vehicle single-track model and considering tire nonlinearity and longitudinal load transfer, a neural network lateral dynamics model is designed based on encoder decoder structure. Among them, the serial arrangement is used to expand the differential equation to describe the incomplete dynamic information, and the hidden layer neurons learn the highly nonlinear and strong coupling characteristics of the vehicle, so as to improve the global calculation accuracy of the model. Use the constructed data set for model training and testing, the results show that, compared with the physical model, the proposed model has higher modeling accuracy under different road adhesion coefficients, and has the ability to implicitly predict road friction conditions. Secondly, using the proposed model to design trajectory tracking control algorithm, according to the vehicle steady-state steering assumption, the required front wheel steering angle and steady-state sideslip angle are calculated, and the steady-state sideslip angle is incorporated into the steering feedback based on path error to realize the reference trajectory tracking control. Finally, comparative analysis of tests under different working conditions is carried out with Simulink/CarSim co-simulation and HiL experiments to evaluate the proposed control algorithm based on neural network model. The results show that the model can realize the accurate tracking control effect of intelligent vehicle at high speed, and has good lateral stability.