基于改进粒子群算法的并联机械手运动参数识别

在工业生产过程中，由于机械手制造与装配过程存在误差，不仅造成运动精度降低，而且阻碍生产效率提高。为解决这一问题，提出了基于了改进粒子群优化算法的并联机械手运动参数识别研究，通过建立参数数学模型，完成机械手几何参数误差的识别和补偿。实验表明，文中提出基于改进粒子群优化算法的并联机械手运动参数识别，测试误差小、收敛速度快，可以为改良生产线、改善产品质量、提高企业效率提供有效的帮助。

Motion Parameter Identification of Parallel Manipulator Based on Particle Swarm Optimization Algorithm

In industrial production, the errors in manufacturing and assembly of manipulators lead to poor movement accuracy and production efficiency. In order to solve the problems, this paper puts forward amotion parameter identification of parallel manipulator based on the particle swarm optimization algorithm, which achieves the manipulator geometric parameter identification and estimation error by the establishment of parameter mathematical model. Experimental results show that motion parameter identification of parallel manipulator based on particle swarm optimization algorithm enjoys small test error and fast convergence, and can be used in the improvement of the production line, product quality, and corporate efficiency.