CLC number: TP39
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-09-06
Cited: 0
Clicked: 6750
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.
@article{title="Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems",
author="Xing-chen Wu, Gui-he Qin, Ming-hui Sun, He Yu, Qian-yi Xu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="9",
pages="1385-1395",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601427"
}
%0 Journal Article
%T Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems
%A Xing-chen Wu
%A Gui-he Qin
%A Ming-hui Sun
%A He Yu
%A Qian-yi Xu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 9
%P 1385-1395
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601427
TY - JOUR
T1 - Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems
A1 - Xing-chen Wu
A1 - Gui-he Qin
A1 - Ming-hui Sun
A1 - He Yu
A1 - Qian-yi Xu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 9
SP - 1385
EP - 1395
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601427
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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