基于迭代优化和神经网络补偿的超冗余机械臂半参数动力学模型辨识  

Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation

作　　者：周宇飞 李中灿 李毅 崔靖凯 贺顺锋 盛展翊 朱明超[1] ZHOU Yufei;LI Zhongcan;LI Yi;CUI Jingkai;HE Shunfeng;SHENG Zhanyi;ZHU Mingchao(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;University of Chinese Academy of Sciences,Beijing 100049,China;Ningxia University,School of Mechanical Engineering,Yinchuan 750021,China)

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]中国科学院大学,北京100049 [3]宁夏大学机械工程学院,宁夏银川750021

出　　处：《光学精密工程》2024年第2期193-207,共15页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.62173047)。

摘　　要：为了实现超冗余机械臂动力学模型的精确辨识,提出了一种基于迭代优化和神经网络补偿的半参数动力学模型辨识方法。首先,介绍了超冗余机械臂的动力学模型和最小参数集,建立了关节非线性摩擦模型,使用遗传算法优化回归矩阵条件数生成激励轨迹。然后建立了机械臂动力学模型物理可行性约束,基于迭代优化方法设计了两层循环网络对超冗余机械臂的惯性参数和关节摩擦模型进行辨识。最后,利用数据集训练BP神经网络,得到超冗余机械臂半参数动力学模型,并与多种算法进行了比较分析。实验结果表明:相较于传统的最小二乘算法和加权最小二乘算法,通过使用本文提出的辨识算法,关节辨识力矩残差均方根(Root Mean Square,RMS)之和分别提高了32.81%和23.76%,半参数动力学模型相比于全参数动力学模型力矩残差均方根之和提高了23.56%,辨识结果验证了辨识方法的有效性和优越性。In order to achieve accurate dynamic model identification of the hyper-redundant manipulator,a semiparametric dynamic model identification method based on iterative optimization and neural network compensation was proposed.First,the dynamic model of the hyper-redundant manipulator and the base parameter set were introduced,joint nonlinear friction model was established,and the excitation trajectory was generated using genetic algorithm to optimize the condition number of the regression matrix.Second,the physical feasibility constraint of the manipulator dynamic model was established,and a two loops identification network was designed to identify the inertial parameters and joint friction model of the hyper-redundant manipulator based on the iterative optimization method.Finally,the BP neural networks were trained to obtain the semiparametric dynamic model of the hyper-redundant manipulator by using data set.A series of identification algorithms were compared and analyzed.The experimental results show that,compared with the traditional least squares algorithm and weighted least squares algorithm,the identification algorithm proposed in this paper can improve the sum of identify torque residual root mean square(RMS)of joints by 32.81% and 23.76%,respectively.The sum of torque residuals of the semi-parametric dynamic model is 23.56% higher than that of the full-parametric dynamic model.The identification results verify the effectiveness of the proposed identification method.

关 键 词：超冗余机械臂 动力学模型辨识 迭代优化 半参数动力学模型 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]