球面4R机构近似轨迹综合的社会认知优化算法

车林仙; 黄勇刚; 杜力

doi:10.16578/j.issn.1004.2539.2020.02.007

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球面4R机构近似轨迹综合的社会认知优化算法

设计计算 | 更新时间：2022-10-25

- 球面4R机构近似轨迹综合的社会认知优化算法
- Social Cognitive Optimization Algorithm for Approximate Path Generation Synthesis of Spherical 4R Linkage
- 机械传动 2020年44卷第2期页码：42-54
- 作者机构：
  
  1.重庆工程职业技术学院智能制造与交通学院，重庆 402260
  2.重庆工商大学制造装备机构设计与控制重庆市重点实验室，重庆 400067
- 作者简介：
  
  车林仙（1971— ），男，四川泸州人，博士，教授，研究方向为机构学、机械优化设计及计算智能等。
- 基金信息：
  
  重庆市基础科学与前沿技术研究专项资助项目(cstc2015jcyjA70006)
- DOI：10.16578/j.issn.1004.2539.2020.02.007
  中图分类号：
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车林仙,黄勇刚,杜力.球面4R机构近似轨迹综合的社会认知优化算法[J].机械传动,2020,44(02):42-54.

Che Linxian,Huang Yonggang,Du Li.Social Cognitive Optimization Algorithm for Approximate Path Generation Synthesis of Spherical 4R Linkage[J].Journal of Mechanical Transmission,2020,44(02):42-54.
车林仙,黄勇刚,杜力.球面4R机构近似轨迹综合的社会认知优化算法[J].机械传动,2020,44(02):42-54. DOI： 10.16578/j.issn.1004.2539.2020.02.007.

Che Linxian,Huang Yonggang,Du Li.Social Cognitive Optimization Algorithm for Approximate Path Generation Synthesis of Spherical 4R Linkage[J].Journal of Mechanical Transmission,2020,44(02):42-54. DOI： 10.16578/j.issn.1004.2539.2020.02.007.

摘要

应用坐标变换原理，推导出球面4R机构连杆点轨迹参数方程。在此基础上，以轨迹误差平方和最小为目标函数，以曲柄存在、杆长协调和传动角限制等为约束条件，建立机构不带预定时标近似轨迹综合的约束优化模型。结合自适应罚函数法，应用社会认知优化（Social Cognitive Optimization，SCO）算法求解该问题。为提高SCO算法的收敛精度和速度，以差分进化（Differential Evolution，DE）操作为邻域搜索算子，设计一种加速社会认知优化（Accelerating Social Cognitive Optimization，ASCO）算法。给出机构综合实例，轨迹分析结果表明，所建立的模型和提出的ASCO算法可行有效。比较了基本SCO、DE和ASCO算法求解该机构综合实例的优化性能，数值实验显示，ASCO算法的收敛速度、收敛精度和稳健性等性能指标优于基本SCO和DE算法。

Abstract

Using the coordinate transformation principle，the parametric equation for the trajectory generated is derived by the coupler point of spherical 4R linkage. On this basis，a constrained optimization model is constructed to formulate the approximate synthesis problem of spherical 4R linkage for the path generation without prescribed timing. The model takes the minimizing sum of squares for path errors as the objective function， and the existing crank，length coordination，and transmission angle restriction as the constraints. Combining the self-adaptive penalty function method，this study employs the social cognitive optimization (SCO) algorithm to solve the aforementioned problem. In order to enhance the convergence speed and accuracy of SCO algorithm，a novel accelerating social cognitive optimization (ASCO) algorithm is proposed in which the differential evolution (DE) operators are used to local search. Numerical examples of mechanism synthesis are given，and trajectory analysis results show that the established model and presented ASCO algorithm are feasible and effective. Furthermore，comparative tests on computational performance to address the examples are implemented with SCO，DE，and ASCO algorithm，and the numerical experiments indicate that ASCO outperforms SCO and DE in terms of robustness，convergence speed and accuracy.

关键词

球面4R机构轨迹综合约束优化设计社会认知优化算法差分进化算法

Keywords

Spherical 4R linkageGenerated path synthesisConstrained optimization designSocial cognitive optimization algorithmDifferential evolution algorithm

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