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Forward kinematics analysis of a six-DOF Stewart platform using PCA and NM algorithm

机译:使用PCA和NM算法对六自由度Stewart平台进行正向运动学分析

摘要

Purpose - The purpose of this paper is to present an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform. Design/methodology/approach - Unlike the convention of developing a set of kinematic equations and then solving them, an alternative numerical algorithm is proposed in which the principal components of link lengths are used as a bridge to analyze the forward kinematics of a Stewart platform. The values of link lengths are firstly transformed to the values of principal components through principal component analysis. Then, the computation of the values of positional variables is transformed to a two-dimensional nonlinear minimization problem by using the relationships between principal components and positional variables. A hybrid Nelder Mead-particle swarm optimizer (NM-PSO) algorithm and a modified NM algorithm are used to solve the two-dimensional nonlinear minimization problem. Findings - Simulation experiments have been conducted to validate the numerical algorithm and experimental results show that the numerical algorithm is valid and can achieve good accuracy and high efficiency. Originality/value - This paper proposes an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.
机译:目的-本文的目的是提出一种用于通用Stewart平台正向运动学分析的自适应数值算法。设计/方法/方法-与开发一组运动学方程然后求解它们的惯例不同,提出了一种替代的数值算法,其中链接长度的主要成分被用作分析Stewart平台正向运动学的桥梁。首先通过主成分分析将链接长度的值转换为主成分的值。然后,通过利用主成分和位置变量之间的关系,将位置变量的值的计算转换为二维非线性最小化问题。混合Nelder Mead-粒子群优化算法(NM-PSO)和改进的NM算法被用于解决二维非线性最小化问题。研究结果-进行了仿真实验,验证了数值算法的有效性,实验结果表明,该数值算法是有效的,可以达到较高的精度和效率。原创性/价值-本文提出了一种自适应数值算法,用于对通用Stewart平台进行正向运动学分析。

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