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

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。

The introduction ofproportional-integral-dorivative （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 bettor 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.