Vehicle Path Following Based on Adaptive Double-Layer Driver Model

被引：0

作者：

Xu, Y. ^{[1
]}

Chi, C. ^{[1
]}

Xu, G. ^{[1
]}

Wei, B. ^{[2
]}

Shen, J. ^{[1
]}

机构：

[1] Shenzhen Univ, Coll Mechatron & Control Engn, Shenzhen, Peoples R China

[2] Shenzhen Univ, Coll Urban Rail Transit, Shenzhen, Peoples R China

来源：

2017 7TH INTERNATIONAL CONFERENCE ON POWER ELECTRONICS SYSTEMS AND APPLICATIONS - SMART MOBILITY, POWER TRANSFER & SECURITY (PESA) | 2017年

基金：

中国国家自然科学基金;

关键词：

Driver model; adapt preview time; particle swarm optimization; coupled dynamics model; critical condition;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The driver model with high efficiency and adaptability to complex path plays an irreplaceable role in vehicle dynamics stability control, active safety and automatic driving control algorithm development. Based on the optimal preview control driver model, a nonlinear double deck driver model considering vehicle time-varying characteristics and driver's dynamic correction is established, and the driver parameters are optimized by particle swarm optimization. The deficiency of the preview follow-up theory is analyzed, a new adaptive strategy of preview time is proposed, and an adaptive function based on the probability distribution and logic curve is constructed. To verify the validity of the model, a complex nonlinear vehicle dynamics model with multiple degrees of freedom is established. According to the simulation test results, the proposed driver model can meet the complex path and critical conditions of the tracking requirements, and it can effectively reduce the tracking error, enhance the stability margin of the closed-loop simulation and reduce the operation burden for the driver at the same time.

引用

页数：8