Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Qiping Chen, Lu Gan, Zhiqiang Jiang, Zhao Xu, Xiaobo Zhang
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Abstract

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.
基于多模式切换的智能车辆多目标自适应巡航控制研究
针对现有自适应巡航控制系统在复杂工况下存在的响应速度慢、舒适性差、油耗高等问题,提出了一种基于多模式切换的智能汽车多目标自适应巡航控制方法。首先,利用分层控制结构设计了自适应巡航控制系统的总体方案,并利用模糊控制理论设计了多模式切换策略,实现了车辆巡航过程中工作模式的划分与切换。其次,基于变间距策略和纵向运动学模型,对安全性、跟随性能、燃油经济性和舒适性等多目标进行了分析和运算,得到了基于多约束条件的二次多目标优化函数。然后,利用基于粒子群优化(PSO)的模型预测控制算法,将多目标函数转化为以预测控制增量为优化变量的标准形式,求解最优控制率。最后,通过设置前车匀速变速、前车快速变速和邻车切入三种复杂工况进行了仿真实验。结果表明,所提出的方法能满足安全性、舒适性和燃油经济性的要求,并能提高智能车辆巡航控制系统的适应性和友好性。
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