基于优化算法的自动驾驶车辆纵向自适应控制

doi:10.16182/j.issn1004731x.joss.19-0372

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (2): 409-420.doi: 10.16182/j.issn1004731x.joss.19-0372

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

基于优化算法的自动驾驶车辆纵向自适应控制

尹智帅, 何嘉雄, 聂琳真, 管家意

武汉理工大学汽车工程学院,湖北武汉 430070

收稿日期:2019-07-23 修回日期:2019-11-25 出版日期:2021-02-18 发布日期:2021-02-20
作者简介:尹智帅(1985-),男,博士,副教授,研究方向为智能网联汽车、驾驶行为。E-mail：zyin@whut.edu.cn
基金资助:
国家重点研发计划(2018YFB0105203),国家自然科学基金(51805388)

Longitudinal Adaptive Control of Autonomous Vehicles Base on Optimization Algorithm

Yin Zhishuai, He Jiaxiong, Nie Linzhen, Guan Jiayi

School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Received:2019-07-23 Revised:2019-11-25 Online:2021-02-18 Published:2021-02-20

摘要/Abstract

摘要： 针对自动驾驶车辆纵向运动的非线性、时变以及不确定性等特性,设计了一种基于粒子群优化(Particle Swarm Optimization, PSO)的径向基神经网络(Radial Basis Function Neural Network, RBFNN)比例积分微分(Proportional Integral Derivative, PID)控制器,RBFNN与PID控制相结合自适应调整PID参数;针对RBFNN和PID的初始参数选择不当会使模型产生超调甚至失稳等问题,采用PSO同时离线优化RBFNN和PID的初始参数,智能选取合理参数;通过在Matlab/Simulink中搭建闭环自适应控制系统模型,新欧洲驾驶循环工况下比较PSO-RBFNN-PID, RBFNN-PID, PID三种控制器的精度和稳定性。仿真结果表明,提出的方法具有更好的控制精度和稳定性,能够很好地实现纵向跟踪控制。

关键词: 自动驾驶车辆, 纵向控制, 自适应, PSO-RBFNN-PID控制器

Abstract: Aiming at the nonlinear, time-varying and uncertain characteristics of the longitudinal motion of autonomous vehicles, a Radial Basis Function neural network(RBFNN) Proportional Integral Derivative(PID) controller base on Particle Swarm Optimization(PSO) is designed. A RBFNN is integrated into a PID controller so that parameters of the PID controller could be adjusted self-adaptively. To solve the problem that poor selection of initial parameters of RBFNN and PID might lead to overshoot or instability in the control system, PSO is adopted to optimize aforementioned initial parameters off-line. Finally, a closed-loop adaptive control system model is built in MATLAB/Simulink. Simulation results show that as compared to the traditional PID controller and a non-optimized RBFNN-PID controller, the proposed PSO-RBFNN-PID controller demonstrates a higher level of controlling accuracy and stability of speed control under the New European Driving Cycle (NEDC) test.

Key words: autonomous vehicle, longitudinal control, self-adaptive, PSO-RBFNN-PID controller

中图分类号:

TP391.9
U46

尹智帅, 何嘉雄, 聂琳真, 管家意. 基于优化算法的自动驾驶车辆纵向自适应控制[J]. 系统仿真学报, 2021, 33(2): 409-420.

Yin Zhishuai, He Jiaxiong, Nie Linzhen, Guan Jiayi. Longitudinal Adaptive Control of Autonomous Vehicles Base on Optimization Algorithm[J]. Journal of System Simulation, 2021, 33(2): 409-420.

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基于优化算法的自动驾驶车辆纵向自适应控制

Longitudinal Adaptive Control of Autonomous Vehicles Base on Optimization Algorithm

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