[1]袁立鹏,许宏光,赵克定.基于改进遗传算法的坦克发动机道路模拟测试平台轨迹寻优[J].东南大学学报(自然科学版),2006,36(2):237-241.[doi:10.3969/j.issn.1001-0505.2006.02.012]
 Yuan Lipeng,Xu Hongguang,Zhao Keding.Track searching of simulating platform of tank engine’s movement state based on improved genetic algorithm[J].Journal of Southeast University (Natural Science Edition),2006,36(2):237-241.[doi:10.3969/j.issn.1001-0505.2006.02.012]
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基于改进遗传算法的坦克发动机道路模拟测试平台轨迹寻优()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
36
期数:
2006年第2期
页码:
237-241
栏目:
自动化
出版日期:
2006-03-20

文章信息/Info

Title:
Track searching of simulating platform of tank engine’s movement state based on improved genetic algorithm
作者:
袁立鹏 许宏光 赵克定
哈尔滨工业大学机电工程学院, 哈尔滨 150001
Author(s):
Yuan Lipeng Xu Hongguang Zhao Keding
School of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, China
关键词:
轨迹 遗传算法 模拟退火法 并联运动系统 坦克发动机
Keywords:
track genetic algorithm simulated annealing parallel manipulator tank engine
分类号:
TP212.12
DOI:
10.3969/j.issn.1001-0505.2006.02.012
摘要:
针对坦克发动机道路模拟测试平台,采用冗余构件多分支并联运动系统结构型式,满足了其大负载、高灵活度、运动复杂的实际要求.鉴于系统结构型式的特殊性,采用基于初始种群渐进漂移的自适应改进模拟退火遗传算法,对此并联运动系统进行六维轨迹寻优解算, 并将系统六维运动轨迹解算仿真结果与实验数据进行对比分析,验证了此系统结构的合理性及所采用算法的有效性.该方法不仅将遗传算法全局规划能力与模拟退火法局域优化特性进行了有效结合,同时通过引入初始种群渐进漂移技术,算法既避免了早熟现象又改善了邻域函数结构和初值鲁棒性,其计算精度达到了0.002 mm 或 0.0015°.
Abstract:
A platform is presented to simulate the tank engine’s movement state as the tank runs on the road. A multi-branches parallel manipulator with redundant branches driven by hydraulic power is adopted, which can meet the requirements of heavy load, high agility and complicated movement. Because of the peculiar structure of the system, this study introduces an improved genetic algorithm involving mechanism of simulated annealing with initial population gradually floating to carry out the six-dimension track searching. The simulation results are compared with the experiment data. The analysis validates that the structure of the system is reasonable and the algorithm is effective. This method perfectly combines the global optimal characteristic of genetic algorithm and the local optimal specialty of simulated annealing algorithm. Furthermore, by applying the technique of initial population gradually floating to the improved genetic algorithm, the algorithm not only avoids premature convergence, but also makes the neighbor-function structure more reasonable. The robust capability of initial value is also improved. The precision of this algorithm can reach the bound of 0.002mm or 0.0015°.

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备注/Memo

备注/Memo:
基金项目: 兵器部大型研制资助项目(502条保).
作者简介: 袁立鹏(1976—)男,博士,讲师, hitylp@126.com.
更新日期/Last Update: 2006-03-20