Structure of non-contact shape control based on hierarchical genetic algorithms

ZHANG Xiao-fei; ZHENG Shi-jie; WANG Hong-tao

doi:10.13224/j.cnki.jasp.2015.12.009

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Structure of non-contact shape control based on hierarchical genetic algorithms

更新时间：2026-07-07

- Structure of non-contact shape control based on hierarchical genetic algorithms
- Vol. 30, Issue 12, Pages: 2876-2881(2015)
- 作者机构：
  
  1. 南京航空航天大学航空宇航学院机械结构力学及控制国家重点实验室,南京,210016
  2. 南京航空航天大学机电学院,南京,210016
- 作者简介：
- 基金信息：
- DOI：10.13224/j.cnki.jasp.2015.12.009
  CLC：
- Published：2015
- 稿件说明：
移动端阅览
张晓飞, 郑世杰, 王宏涛. 基于递阶遗传算法的结构非接触形状控制[J]. 航空动力学报, 2015, 30(12): 2876-2881.

doi: 10.13224/j.cnki.jasp.2015.12.009
张晓飞, 郑世杰, 王宏涛. 基于递阶遗传算法的结构非接触形状控制[J]. 航空动力学报, 2015, 30(12): 2876-2881. DOI： 10.13224/j.cnki.jasp.2015.12.009.

doi: 10.13224/j.cnki.jasp.2015.12.009 DOI：

摘要

以PLZT光致伸缩层合梁非接触形状控制问题为研究对象

提出了一种结合结构拓扑优化与递阶遗传算法的控制方法.该方法以PLZT光致伸缩驱动器的拓扑分布和照射的光强值为设计变量

以PLZT光致伸缩层合梁的期望形状与控制形状的差值函数为适应度函数

应用结构拓扑优化、递阶遗传算法和有限元法

优化了PLZT光致伸缩驱动器的分布和所照射的光强值.与基于传统遗传算法的形状控制方法的计算结果进行了对比分析

该方法的进化速度提高了91%以上

所控制的结构形状与期望形状的误差较传统遗传算法降低了76%以上.

Abstract

Taking the non-contact shape control of a beam bonded with PLZT photostrictive actuators on its surface as an object

a control method was proposed by combining the structural topology optimization and hierarchical genetic algorithms. In this method

the topological distribution of photostrictive actuators and the light intensity were chosen as design variables

and the shape errors between the desired shape and the achieved shape of beam bonded with PLZT photostrictive actuators were taken as the fitness function. The distribution of PLZT photostrictive actuators and the light intensity were optimized by using structural topology optimization

hierarchical genetic algorithms and finite element method. The performance of the presented method for shape control was assessed in comparison with that of the conventional genetic algorithm. The results demonstrate that the speed of the evolution of the presented method has been improved by more than 91% and the errors between the desired shape and the achieved shape have been reduced by over 76% compared with the conventional genetic algorithm.

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Related Institution

State Key Laboratory of Mechanical Transmission, Chongqing University

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