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平面四杆机构运动轨迹多目标综合优化及误差仿真研究 被引量:6

Research on Multi-objective Synthetic Optimization and Error Simulation of Motion Trajectory of Planar Four-bar Linkage
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摘要 针对平面四杆机构运动轨迹所产生的误差较大问题,引用改进差分进化算法对平面四杆机构运动轨迹误差进行优化。构造平面四杆机构运动简图,分析四杆机构变量参数。通过欧几里得距离误差函数推导出优化目标函数,采用改进差分进化算法对四杆机构约束条件进行多目标综合优化。结合具体实例,将优化后结果输入到Matlab软件进行误差仿真,并且与优化前误差仿真结果进行对比和分析。仿真结果表明:优化前,四杆机构运动轨迹所产生的横向和纵向误差峰值分别为0.25cm和0.19cm,优化后,四杆机构运动轨迹所产生的横向和纵向误差峰值分别为0.17cm和0.13cm,横向和纵向误差峰值分别降低了32.0%和31.6%。采用改进差分进化算法优化四杆机构运动轨迹,可以降低四杆机构运动轨迹产生的误差。 Aiming at the large error of the motion of the planar four-bar mechanism,an improved differential evolution algorithm is used to optimize the trajectory error of planar four-bar mechanism. Structure four-bar mechanism,a nd analyze the variable parameters of four-bar mechanism. By using Euclidean distance error function, the optimal objective function is deduced,and an improved differential evolution algorithm is used to optimize the constraints of the four-bar linkage mechanism. The optimized results are input to the Matlab software for error simulation,and compared with the pre-optimization error simulation results. The simulation results show that the transverse and longitudinal error peaks of the four-bar mechanism are 0. 25 cm and0. 19 cm respectively,and the peak of the transverse and longitudinal errors are 0. 17 cm and 0. 13 cm,and the horizontal and vertical error peak values decreased by 32. 0% and 31. 6% respectively. Using the improved differential evolution algorithm to optimize the trajectory of the four-bar mechanism,the error generated by the trajectory of the four-bar mechanism can be reduced.
作者 王琦 何仁 WANG Qi HE Ren(Department of Automotive Engineering, Zhenjiang College, Jiangsu Zhenjiang 212003, China School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China)
出处 《组合机床与自动化加工技术》 北大核心 2017年第9期55-58,共4页 Modular Machine Tool & Automatic Manufacturing Technique
基金 国家自然科学基金项目(51275212) 江苏省高等职业院校教师国内高级访问学者计划资助项目(2015FX098)
关键词 平面四杆机构 改进差分进化 运动轨迹 多目标优化 Planar four-bar mechanism Improved differential evolution trajectory multi-objective optimization
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