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Robust Model-based Predictive Control for a Cooperative Cycling Cyber-physical System

机译:协作自行车网络物理系统的基于模型的鲁棒预测控制

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Thanks to the massive adoption of bicycles in many countries, congestion issues over the cycling infrastructure are becoming common. When the congestion occurs, the natural formation of platoons brings benefits in terms of efficient use of cycling roads; however, it also brings concerns in terms of security of cyclists due to a lack of explicit coordination. In this context, previous solutions have proposed group cycling strategies supported by a platoon-based cyber-physical system, but the system encountered problems in terms of spacing control, in particular for large group sizes or highly variable group leader speeds. In this work, we propose a Robust Model-based Predictive Control (RMPC) with fuzzy intervals models strategy for the coordination of a cyber-physical system for bicycle platoons, in order to maintain a constant space policy under human-related physical limitations and human misinterpretation of the system's acceleration signals. From the analysis made using simulations, we demonstrate the proposed RMPC mechanism outperforms the existing sliding surface control method employed in the group-cycling strategy.
机译:由于在许多国家大量使用自行车,自行车基础设施上的交通拥堵问题变得越来越普遍。当拥堵发生时,排的自然形成会带来有效利用自行车道的好处;然而,由于缺乏明确的协调,这也给骑车人的安全带来了担忧。在这种情况下,先前的解决方案已经提出了一种基于排的电子物理系统支持的小组循环策略,但是该系统在间隔控制方面遇到了问题,特别是对于大型小组规模或高度可变的小组领导者速度。在这项工作中,我们提出了一种基于模糊模型的鲁棒模型预测控制(RMPC)策略,用于协调自行车排的网络物理系统,以便在人类相关的物理限制和人类的影响下保持恒定的空间策略。对系统加速信号的误解。从使用模拟进行的分析中,我们证明了提出的RMPC机制优于群体循环策略中使用的现有滑动表面控制方法。

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