【摘要】：為解決將PID控制器引入?yún)f(xié)同碰撞避免(cooperative collision avoidance system,CCAS)的研究中存在的不能合理優(yōu)化PID控制器,以及對車輛行駛穩(wěn)定性、舒適性及燃油經(jīng)濟性研究不足的問題,本文提出使用改進的粒子群優(yōu)化算法(particle swarm optimization,PSO)優(yōu)化PID控制器的方法,來實現(xiàn)CCAS對車輛更好的操控的目標。首先,本文使用PRESCAN和MATLAB/Simulink進行聯(lián)合仿真,構(gòu)建了由PID控制器,機動策略判斷模塊組成的CCAS。其次,本文使用改進的粒子群算法,依據(jù)獲得的汽車動力學數(shù)據(jù),對PID控制器進行了優(yōu)化。最后,本文模擬了配備CCAS的車輛在其PID控制器經(jīng)過優(yōu)化前后,在低速(≤50 km/h)和高速(≥100 km/h)兩種巡航狀態(tài)下,進行減速行駛、減速轉(zhuǎn)向工況的測試。結(jié)果表明,經(jīng)過本文方法優(yōu)化的PID控制器,不僅可使CCAS實現(xiàn)基本功能,還可實現(xiàn)車輛動態(tài)穩(wěn)定性,行駛舒適性和燃油經(jīng)濟性的改善。
[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，

本文編號：2254015