Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design...Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.展开更多
为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹...为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹采集数据。为提升动力学参数辨识的准确性,在加权最小二乘法基础上进行改进,提出了IRLS算法对动力学参数进行辨识。最后以六自由度机器人为试验对象,进行了参数辨识试验。结果表明,基于IRLS算法的辨识方法与加权最小二乘法相比,前3个关节力矩误差的均方根(Root Mean Square,RMS)值降低了13.28%,后3个关节力矩误差的RMS值降低了28.57%,6个关节力矩误差的RMS值平均降低了17.15%,证明了基于IRLS算法的辨识方法的有效性。展开更多
基金Project supported by the National Natural Science Foundation of China(No.61603322)the Research Foundation of Education Bureau of Hunan Province of China(No.16C1542)
文摘Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.
文摘为提高机器人动力学参数辨识的准确性,提出了一种基于迭代加权最小二乘(Iterative Reweighted Least Squares,IRLS)算法的辨识方法。首先推导了机器人的线性动力学模型,随后提出了一种改进摩擦模型,并设计了改进傅里叶级数作为激励轨迹采集数据。为提升动力学参数辨识的准确性,在加权最小二乘法基础上进行改进,提出了IRLS算法对动力学参数进行辨识。最后以六自由度机器人为试验对象,进行了参数辨识试验。结果表明,基于IRLS算法的辨识方法与加权最小二乘法相比,前3个关节力矩误差的均方根(Root Mean Square,RMS)值降低了13.28%,后3个关节力矩误差的RMS值降低了28.57%,6个关节力矩误差的RMS值平均降低了17.15%,证明了基于IRLS算法的辨识方法的有效性。
