| 基于改进粒子群算法的3-RPS并联机构正解研究 |
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| 引用本文： | 张伟,高洪,陈玉,钱炳南.基于改进粒子群算法的3-RPS并联机构正解研究[J].井冈山大学学报（自然科学版）,2016(4):63-67. |
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| 作者姓名： | 张伟 高洪 陈玉 钱炳南 |
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| 作者单位： | 安徽工程大学机械与汽车工程学院, 安徽, 芜湖 241000,安徽工程大学机械与汽车工程学院, 安徽, 芜湖 241000,安徽工程大学机械与汽车工程学院, 安徽, 芜湖 241000,安徽大昌科技股份有限公司, 安徽, 芜湖 241000 |
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| 基金项目： | 安徽省自然科学基金项目（1308085ME78）；2015年安徽省科技攻关计划项目（150102060） |
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| 摘 要： | 为快速准确求解3-RPS并联机构运动学正解,将其化归为非线性方程组求解问题,又基于优化理论将其转化成多目标优化问题,并以加权法将多目标问题转化为单目标优化问题,最后采用改进粒子群算法进行数值求解,最后给出了算例。仿真结果表明:该方法适用于求解并联机构的正解问题,其收敛速度和计算精度较标准PSO算法有明显改善。
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| 关 键 词： | 改进粒子群算法 并联机构 运动学正解 RPS |
| 收稿时间： | 2015/10/17 0:00:00 |
| 修稿时间： | 1/6/2016 12:00:00 AM |
FORWARD KINEMATICS STUDY OF 3-RPS PARALLEL MECHANISM BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION |
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| ZHANG Wei,GAO Hong,Chen Yu and Qian Bing-nan.FORWARD KINEMATICS STUDY OF 3-RPS PARALLEL MECHANISM BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION[J].Journal of Jinggangshan University(Natural Sciences Edition),2016(4):63-67. |
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| Authors: | ZHANG Wei GAO Hong Chen Yu and Qian Bing-nan |
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| Institution: | School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China,School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China,School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China and Anhui Dachang Technology Incorporated Company, Wuhu, Anhui 241000, China |
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| Abstract: | In order to solve the problem of parallel mechanism forward kinematics quickly and accurately, we proposed a method that translated it into the problem of nonlinear equations. Furthermore, we translated it into a multi-objective optimization problem based on optimization theory. The weighting method is proposed to translate the multi-objective problem into a single objective optimization problem. An improved particle swarm optimization algorithm was put forward to solve the above optimization model. Finally, a numerical example is given. Simulation results show that the method is suitable for solving the parallel mechanism forward kinematics, and convergence speed and accuracy of calculation of the method are significantly improved compared with the standard PSO algorithm. |
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| Keywords: | Improved particle swarm optimization parallel mechanism forward kinematics RPS |
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