巡航控制
对偶(语法数字)
控制理论(社会学)
车头时距
计算机科学
自适应控制
控制器(灌溉)
趋同(经济学)
模型预测控制
巡航
观察员(物理)
控制工程
控制(管理)
工程类
模拟
人工智能
艺术
物理
文学类
量子力学
航空航天工程
农学
经济
生物
经济增长
作者
Zhaolun Li,Jingjing Jiang,Wen-Hua Chen
标识
DOI:10.1109/icm54990.2023.10102091
摘要
Inspired by the recent work on dual control for exploration and exploitation (DCEE), this paper presents a solution to adaptive cruise control problems via a dual control approach. Different from other adaptive controllers, the proposed dual model predictive control not only uses the current and future inputs to keep a constant headway distance between the leading vehicle and the ego vehicle but also tries to reduce the uncertainty of state estimation by actively learning the surrounding environment as well, which leads to faster convergence of the estimated parameters and better reference tracking performance. The simulation results demonstrate that the proposed dual control framework outperforms a conventional model predictive controller with disturbance observer for adaptive cruise control with unknown road grade.
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