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Sensors (Basel). 2017 May 30;17(6). pii: E1244. doi: 10.3390/s17061244.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

Han G1,2, Fu W3, Wang W4, Wu Z5,6.

Author information

1
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China. han_gn@163.com.
2
School of Computer, Xianyang Normal University, Xianyang 712000, China. han_gn@163.com.
3
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China. weipingf@xaut.edu.cn.
4
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China. wangwen@xaut.edu.cn.
5
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China. wuzs2005@163.com.
6
School of Computer, Xianyang Normal University, Xianyang 712000, China. wuzs2005@163.com.

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.

KEYWORDS:

PID control; forgetting factor recursive least square; intelligent vehicle; neural network; path tracing; steer control

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