| 引用本文: | 章军辉,李庆,陈大鹏.仿驾驶员多目标决策自适应巡航鲁棒控制[J].控制理论与应用,2018,35(6):769~777.[点击复制] |
| ZHANG Jun-hui,LI Qing,CHEN Da-peng.Drivers imitated multi-objective adaptive cruise control algorithm[J].Control Theory and Technology,2018,35(6):769~777.[点击复制] |
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| 仿驾驶员多目标决策自适应巡航鲁棒控制 |
| Drivers imitated multi-objective adaptive cruise control algorithm |
| 摘要点击 2595 全文点击 1486 投稿时间：2017-08-17 修订日期：2017-12-07 |
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| DOI编号 10.7641/CTA.2017.70585 |
| 2018,35(6):769-777 |
| 中文关键词 半自动驾驶 自适应巡航控制 模型预测控制 松弛向量 跟驰习惯 |
| 英文关键词 semi-automatic driving adaptive cruise control model predictive control slack variable vector car-following behavior habits |
| 基金项目 中国科学院科技服务网络计划(STS计划), 面向智能驾驶的新能源汽车电子开放平台建设与产业化项目(KFJ–STS–ZDTP–045)资助. |
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| 中文摘要 |
| 针对目前市场上自适应巡航(adaptive cruise control, ACC)用户使用率低与驾乘人员接受度差的问题, 发展
了一种多目标自适应巡航控制算法, 基于模型预测控制(model predictive control, MPC)理论, 综合协调动态追踪
性、燃油经济性、驾乘舒适性以及跟车安全性四项存在着一定冲突的控制目标, 采用二次型性能指标以及线性不等
式约束的形式, 将纵向期望加速度决策问题转化成带约束的在线二次规划问题. 引入误差修正项, 建立闭环反馈校
正机制, 以补偿模型失配带来的预测误差, 同时采用松弛向量法扩展求解可行域, 规避了硬约束下二次规划非可行
解问题. 进一步, 各工况下所强化的性能指标与约束空间不同, 采用性能指标权重微校与约束空间边界松弛的策略,
设计出3种工作模式ACC, 以适应熟练驾驶群体的跟驰习惯. 仿真结果表明, 前车组合工况下, 多模式多目标ACC
控制能够实现各工作模式之间的无缝切换, 并实现良好的期望跟车目的, 从而增强ACC系统对复杂道路交通环境的
适应性. |
| 英文摘要 |
| On the issue of low utilization and acceptance of commercial adaptive cruise control (ACC), a multi-objective
adaptive cruise control (MO–ACC) algorithm is developed in this paper. Based on model predictive control (MPC) theory,
comprehensively considering the coordination among various conflicting objectives such as tracking capability, fuel economy,
ride comfort and rear-end safety, the decision of desired longitudinal acceleration is transformed into online quadratic
programming (QP) problem which could be formulated as a quadratic cost function with linear multi-constraints. In order to
compensate for prediction error caused by modeling mismatch, the robustness of control system is improved by introducing
an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible
solution owing to hard constraints during the process of optimization. Further, under different kinds of traffic scenarios,
the focusing performance index along with constraint space varies, and therefore different ACC modes are established to
meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as
slack relaxation. The simulations show that under multiple traffic scenarios of preceding car, the following car can realize
seamless switching among various working modes, and also is able to achieve the good expectation during car-following,
which will help to enhance the adaptability of the ACC system against the complex road traffic environment. |
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