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兵工学报 ›› 2015, Vol. 36 ›› Issue (6): 1117-1122.doi: 10.3969/j.issn.1000-1093.2015.06.022

• 研究简报 • 上一篇    下一篇

考虑区间不确定性的某弹丸提升装置参数优化

蒋清山, 钱林方, 徐亚栋, 邹权   

  1. (南京理工大学 机械工程学院, 江苏 南京 210094)
  • 收稿日期:2014-06-30 修回日期:2014-06-30 上线日期:2015-08-03
  • 作者简介:蒋清山(1986—),男,博士研究生
  • 基金资助:
    国防基础科研项目(A2620133003)

Parameter Optimization of a Shell Elevating Device with Interval Uncertainties

JIANG Qing-shan, QIAN Lin-fang, XU Ya-dong, ZOU Quan   

  1. (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China)
  • Received:2014-06-30 Revised:2014-06-30 Online:2015-08-03

摘要: 针对某自动装填系统弹丸提升装置在使用过程中出现的性能下降问题,提出一种考虑区间不确定性的提升装置参数优化方法。基于动力学分析软件ADAMS建立提升装置的机电液耦合模型,将液压系统参数和摩擦系数作为区间不确定量,利用区间数序关系将构件载荷及其波动区间最小转化为确定性优化目标。采用拉丁超立方抽样(LHS)技术进行抽样仿真,基于径向基函数(RBF)神经网络方法构建了提升装置的近似模型,大大提高了优化求解效率。基于多岛遗传算法和序列二次规划法的双层嵌套优化方法求得系统的鲁棒解,仿真结果与试验结果均验证了该方法在解决自动装填系统等复杂问题中的有效性和适用性。

关键词: 兵器科学与技术, 自动装填系统, 提升装置, 参数优化, 区间不确定, 径向基函数神经网络

Abstract: An optimization method for shell elevating device with interval uncertainties is proposed to solve the problem of performance degradation during operating. A multidisciplinary model based on ADAMS is established. The parameters of hydraulic system and the friction coefficient are regarded as the interval uncertainties, and the minimal loading and fluctuation on the mechanism are set as the objectives by using the interval theory. Simulations are carried out on Latin hypercube sampling (LHS) points. An approximate model of shell elevating device is constructed based on radial basis function (RBF) neural networks to improve the efficiency. The nested optimization method based on multi-island GA and NLPQL is used to obtain the robust solutions. The simulation and experimental results demonstrate the efficiency and adaptability of the proposed method for solving the complex problems in automatic loading system.

Key words: ordnance science and technology, automatic loading system, elevating device, parameter optimization, interval uncertainty, radial basis function neural network

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