Paper Info
Title
Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems.
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.
Year
DOI
Venue
2017
10.1631/FITEE.1601427
Frontiers of IT & EE
Keywords
Field
DocType
Cooperative collision avoidance system (CCAS), Improved particle swarm optimization (PSO), PID controller, Vehicle comfort, Fuel economy, TP39
Motion planning,Particle swarm optimization,MATLAB,PID controller,Computer science,Control theory,Vehicle driving,Collision,Acceleration
Journal
Volume
Issue
ISSN
18
9
2095-9184
Citations 
PageRank 
References 
0
0.34
10
Authors
5
Name
Order
Citations
PageRank
Xing-chen Wu100.68
Guihe Qin2239.00
Ming-Hui Sun383.83
He Yu443.14
Qian-yi Xu500.34