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

Xing-chen WU; Gui-he QIN; Ming-hui SUN; He YU; Qian-yi XU

doi:10.1631/FITEE.1601427

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

Regular Papers | Updated：2022-05-19

- Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 9, Pages: 1385-1395(2017)
- Affiliations：
  
  College of Computer Science and Technology, Jilin University, Changchun 130012, China
  MOE Key Laboratory of Symbol Computation and Knowledge Engineering, Changchun 130012, China
  Department of Measurement and Controlling Engineering, Changchun University, Changchun 130012, China
- Author bio：
  
  Ming-hui SUN, E-mail: 511518984@qq.com
- Funds：
- DOI：10.1631/FITEE.1601427
  CLC： TP39
- Received：20 July 2016，
  
  Revised：2017-;9-;6，
  
  Published：2017-09
- Accepted：
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Xing-chen WU, Gui-he QIN, Ming-hui SUN, et al. 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.
DOI：

Xing-chen WU, Gui-he QIN, Ming-hui SUN, et al. 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. DOI： 10.1631/FITEE.1601427.

摘要

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。

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.

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references

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