Abstract
The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.
Highlights
In recent years, intelligent vehicles play an important role in the intelligent transportation system
This paper focuses on motion tracking control, which is mostly dedicated to the lateral control
This work designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control
Summary
Intelligent vehicles play an important role in the intelligent transportation system. The neural networks PID controller can achieve good results in controlling an intelligent vehicle, and provide safe driving This control technique has proven to be robust against system parameter variations. Based on the previous research [42,43] in the path planning adopting behavior dynamics method, using the established neural network PID controller, the intelligent vehicle planning tracing controlling is realized.
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