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CLC number: TP39

On-line Access: 2017-10-25

Received: 2016-07-20

Revision Accepted: 2017-01-23

Crosschecked: 2017-09-06

Cited: 0

Clicked: 1250

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ming-hui Sun

http://orcid.org/0000-0002-1809-8187

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1385-1395

10.1631/FITEE.1601427


Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems


Author(s):  Xing-chen Wu, Gui-he Qin, Ming-hui Sun, He Yu, Qian-yi Xu

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   511518984@qq.com

Key Words:  Cooperative collision avoidance system (CCAS), Improved particle swarm optimization (PSO), PID controller, Vehicle comfort, Fuel economy


Xing-chen Wu, Gui-he Qin, Ming-hui Sun, He Yu, Qian-yi Xu. Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1385-1395.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601427"
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Abstract: 
The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

基于改进粒子群算法优化的PID控制器在协同碰撞避免系统中的应用

概要:为解决将PID控制器引入协同碰撞避免(cooperative collision avoidance system, CCAS)的研究中存在的不能合理优化PID控制器,以及对车辆行驶稳定性、舒适性及燃油经济性研究不足的问题,本文提出使用改进的粒子群优化算法(particle swarm optimization, PSO)优化PID控制器的方法,来实现CCAS对车辆更好的操控的目标。首先,本文使用PRESCAN和MATLAB/Simulink进行联合仿真,构建了由PID控制器,机动策略判断模块组成的CCAS。其次,本文使用改进的粒子群算法,依据获得的汽车动力学数据,对PID控制器进行了优化。最后,本文模拟了配备CCAS的车辆在其PID控制器经过优化前后,在低速(≤50 km/h)和高速(≥100 km/h)两种巡航状态下,进行减速行驶、减速转向工况的测试。结果表明,经过本文方法优化的PID控制器,不仅可使CCAS实现基本功能,还可实现车辆动态稳定性,行驶舒适性和燃油经济性的改善。

关键词:协同碰撞避免系统;改进的粒子群算法;PID控制器;行驶舒适性;燃油经济性

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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