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（英文）
发布时间：2018-10-05 16:05
【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]:In order to solve the problem that the PID controller can not be optimized reasonably in the research of (cooperative collision avoidance system,CCAS, and the research on vehicle driving stability, comfort and fuel economy is insufficient. In this paper, an improved particle swarm optimization algorithm (particle swarm optimization,PSO) is proposed to optimize the PID controller to achieve the goal of better vehicle control by CCAS. First of all, this paper uses PRESCAN and MATLAB/Simulink to carry on the joint simulation, constructs the CCAS. which is composed of the PID controller, the maneuver strategy judgment module. Secondly, the improved particle swarm optimization algorithm is used to optimize the PID controller according to the obtained vehicle dynamics data. Finally, this paper simulates the tests of deceleration and steering of vehicles with CCAS under two cruising conditions: low speed (鈮，
本文编号：2254015
[Abstract]:In order to solve the problem that the PID controller can not be optimized reasonably in the research of (cooperative collision avoidance system,CCAS, and the research on vehicle driving stability, comfort and fuel economy is insufficient. In this paper, an improved particle swarm optimization algorithm (particle swarm optimization,PSO) is proposed to optimize the PID controller to achieve the goal of better vehicle control by CCAS. First of all, this paper uses PRESCAN and MATLAB/Simulink to carry on the joint simulation, constructs the CCAS. which is composed of the PID controller, the maneuver strategy judgment module. Secondly, the improved particle swarm optimization algorithm is used to optimize the PID controller according to the obtained vehicle dynamics data. Finally, this paper simulates the tests of deceleration and steering of vehicles with CCAS under two cruising conditions: low speed (鈮，
本文编号：2254015
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