The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking\ncontroller is one of the key technologies in intelligent vehicle research. This paper mainly designs a\nlateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control.\nFirstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle\nparameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled\nAuto-Regression and Moving-Average) model, a second-order control system model is built. Using\nforgetting factor recursive least square estimation (FFRLS), the system parameters are identified.\nFinally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path\ntracking control based on the vehicle model and the steering system model. Experimental simulation\nresults show that the proposed model and algorithm have the high real-time and robustness in path\ntracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation\ntracking control, and lays the foundation for the vertical and lateral coupling control.
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