The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network
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. Fi...
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doaj-a9a73bb09ee640d2b240011f2b74cc392020-11-25T01:30:18ZengMDPI AGSensors1424-82202017-05-01176124410.3390/s17061244s17061244The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural NetworkGaining Han0Weiping Fu1Wen Wang2Zongsheng Wu3School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe 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.http://www.mdpi.com/1424-8220/17/6/1244intelligent vehiclesteer controlforgetting factor recursive least squareneural networkPID controlpath tracing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gaining Han Weiping Fu Wen Wang Zongsheng Wu |
spellingShingle |
Gaining Han Weiping Fu Wen Wang Zongsheng Wu The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network Sensors intelligent vehicle steer control forgetting factor recursive least square neural network PID control path tracing |
author_facet |
Gaining Han Weiping Fu Wen Wang Zongsheng Wu |
author_sort |
Gaining Han |
title |
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network |
title_short |
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network |
title_full |
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network |
title_fullStr |
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network |
title_full_unstemmed |
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network |
title_sort |
lateral tracking control for the intelligent vehicle based on adaptive pid neural network |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-05-01 |
description |
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. |
topic |
intelligent vehicle steer control forgetting factor recursive least square neural network PID control path tracing |
url |
http://www.mdpi.com/1424-8220/17/6/1244 |
work_keys_str_mv |
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