文章基本信息

标题：Optimization of hinge point in luffing mechanism of aerial working vehicle based on PSO algorithm
本地全文：下载
作者：Yongjie Xu ; Bangsheng Xing ; Lei Cai 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：233
页码：4034
DOI：10.1051/e3sconf/202123304034
出版社：EDP Sciences
摘要：Luffing mechanism is one of the important parts of aerial working vehicle, which plays a decisive role in the overall stability of boom system, the force of luffing cylinder and the force at each hinge point position. In this paper, the five hinge point luffing mechanism of aerial working vehicle is taken as the research object, and the force of its luffing cylinder under dangerous conditions is optimized. By studying the working principle of the boom system and the force analysis of the luffing mechanism, and then establishing the mechanical model, the objective function was optimized based on particle swarm algorithm and Matlab. The optimization results show that the maximum force on the luffing cylinder decreases by 18.9% with the optimized hinge points, which greatly improves the performance of the whole machine and provides a reference for the application of particle swarm optimization in construction machinery.
其他摘要：Luffing mechanism is one of the important parts of aerial working vehicle, which plays a decisive role in the overall stability of boom system, the force of luffing cylinder and the force at each hinge point position. In this paper, the five hinge point luffing mechanism of aerial working vehicle is taken as the research object, and the force of its luffing cylinder under dangerous conditions is optimized. By studying the working principle of the boom system and the force analysis of the luffing mechanism, and then establishing the mechanical model, the objective function was optimized based on particle swarm algorithm and Matlab. The optimization results show that the maximum force on the luffing cylinder decreases by 18.9% with the optimized hinge points, which greatly improves the performance of the whole machine and provides a reference for the application of particle swarm optimization in construction machinery.