基于粒子群寻优的汽车自适应巡航预测控制  

Adaptive Cruise Predictive Control Based on Particle Swarm Optimization

作　　者：周稼铭 张亮修 衣丰艳[3] 彭剑坤 ZHOU Jiaming;ZHANG Liangxiu;YI Fengyan;PENG Jiankun(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;Shanghai Bao Long Automotive Corporation,Shanghai 201619,China;School of Automotive Engineering,Shandong Jiaotong University,Ji’nan,Shandong 250357,China;School of Transportation,Southeast University,Nanjing,Jiangsu 211102,China)

机构地区：[1]北京理工大学机械与车辆学院,北京100081 [2]上海保隆汽车科技股份有限公司,上海201619 [3]山东交通学院汽车工程学院,山东济南250357 [4]东南大学交通学院,江苏南京211102

出　　处：《北京理工大学学报》2021年第2期214-220,共7页Transactions of Beijing Institute of Technology

基　　金：国家重点研发计划子课题资助项目(2018YFB0105900);山东省自然科学基金资助项目(ZR2019MEE029);山东省农机装备研发创新计划资助项目(2018YF018)。

摘　　要：为进一步提升多目标自适应巡航系统预测控制精度,提出一种基于粒子群寻优的汽车自适应巡航预测控制算法.首先建立一种包含前车加速度扰动的自适应巡航系统车间纵向运动学模型,并对其线性离散化;其次综合车距误差、相对车速、自车加速度和冲击度,设计二次型多目标优化性能指标函数和多参数约束条件,构建自适应巡航预测控制优化命题;最后为便于问题求解,将目标函数和约束条件推导转化为以预测控制增量为优化变量的规范形式,并基于粒子群优化算法求解自适应巡航预测控制的最优控制律.通过Matlab/Simulink多工况仿真结果表明,粒子群算法求解的最优控制律能够控制自车保持更好的跟踪性和自适应性.To further improve the predictive control accuracy of multi-objective adaptive cruise system,an adaptive cruise predictive control algorithm based on particle swarm optimization was proposed.Firstly,a longitudinal kinematics model with front vehicle acceleration disturbance of adaptive cruise system was established and linearly discretized.Then,synthesizing the distance error,relative speed,acceleration and impact,a quadratic multi-objective optimization performance index function and multi-parameter constraints were designed,and an adaptive cruise predictive control optimization problem was constructed.Finally,in order to solve the problem easily,the objective function and constraints were deduced into a normative form with predictive control increment as the optimization variable,and the optimal control law of adaptive cruise predictive control was solved based on particle swarm optimization algorithm.The simulation results of Maltab/Simulink under multiple working conditions show that the optimal control law solved by particle swarm optimization algorithm can control the self-driving vehicle to maintain better tracking and self-adaptability.

关 键 词：自适应巡航系统 预测控制 粒子群优化 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]