基于非线性偏最小二乘的操作臂逆解算法  

Inverse Kinematics Solution of Manipulator Based on Nonlinear PLS

作　　者：费冬冬[1] 邓琛[1] 张琴舜[2] 黄祖良[1] 

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620 [2]上海交通大学核科学与工程学院,上海200240

出　　处：《轻工机械》2015年第2期14-17,22,共5页Light Industry Machinery

基　　金：国家自然科学基金项目(61201244);中央财政专项资金项目资助(ye-13-3-2);上海工程技术大学研究生创新项目资助(E1-0903-14-01036)

摘　　要：操作臂逆运动学问题是机器人控制中的一项重要内容。目前使用较多的神经网络法大多为多输入多输出或者多输入单输出方式,需要大量运算。非线性偏最小二乘法(NLPLS)建立的模型分为内部和外部模型,样本数据经外部模型处理后才用于训练若干个单输入单输出的神经网络。对PUMA560操作臂的仿真试验表明,在相同隐层神经元数的情况下,该算法比普通神经网络法具有更好的预测精度。这也表明,NLPLS只需较少的隐层神经元数就可以达到普通方法的精度,从而减少运算量。The inverse kinematics of manipulator is a vital problem in robot control.Generally,most of the neural networks are applied by many researchers as numerical solutions with multi-input-multi-output or multi-input-sigle-output ones,and that means a large amount of computations.Models built by NLPLS were divided into outer models and inner models.The samples were used for the training process of several neural networks after they were processed by outer models.Simulations based on the PUMA 560 manipulator show that NLPLS has a better prediction accuracy than ordinary solutions when they have the same number of hidden units.The results also indicate that,by applying NLPLS,less computation is needed to meet the same accuracy requirements.

关 键 词：机器人 操作臂 非线性偏最小二乘 逆运动学 

分 类 号：TP241.2[自动化与计算机技术—检测技术与自动化装置]