[1] WU J, WANG J S, YOU Z. An overview of dynamic parameter identification of robots[J]. Robotics and Computer-Integrated Manufacturing, 26, 414-419(2010).

[2] [2] 马天兵， 宫晗， 杜菲， 等. 基于线结构光和优化PID的压电柔性机械臂振动控制［J］. 光学 精密工程， 2021， 29（11）： 141. doi: 10.37188/OPE.2021.0207MAT B， GONGH， DUF， et al. Piezoelectric flexible manipulator vibration control based on line structured light and optimized PID［J］. Opt. Precision Eng.， 2021， 29（11）： 141.（in Chinese）. doi: 10.37188/OPE.2021.0207

[3] LI Y, LI Y H, ZHU M C et al. A nonlinear momentum observer for sensorless robot collision detection under model uncertainties[J]. Mechatronics, 78, 102603(2021).

[4] [4] 韩勇. 协作机器人模型辨识方法与人机交互控制技术研究［D］. 上海： 上海交通大学， 2020.HANY. Model Identification and Human-Robot Interaction Control of Cobots［D］. Shanghai： Shanghai Jiao Tong University， 2020.

[5] DEHIO N, SMITH J, WIGAND D L et al. Enabling impedance-based physical human–multi–robot collaboration： experiments with four torque-controlled manipulators[J]. The International Journal of Robotics Research, 41, 68-84(2022).

[6] JIN J, GANS N. Parameter identification for industrial robots with a fast and robust trajectory design approach[J]. Robotics and Computer Integrated Manufacturing, 31, 21-29(2015).

[7] GAUTIER M, KHALIL W. Direct calculation of minimum set of inertial parameters of serial robots[J]. IEEE Transactions on Robotics and Automation, 6, 368-373(1990).

[8] [8] 崔靖凯， 赛华阳， 张恩阳， 等. 基于灰狼算法的模块化关节摩擦辨识和补偿［J］. 光学 精密工程， 2021， 29（11）： 2683-2691. doi: 10.37188/OPE.20212911.2683CUIJ K， SAIH Y， ZHANGE Y， et al. Identification and compensation of friction for modular joints based on grey wolf optimizer［J］. Opt. Precision Eng.， 2021， 29（11）： 2683-2691.（in Chinese）. doi: 10.37188/OPE.20212911.2683

[9] HAN Y, WU J H, LIU C et al. An iterative approach for accurate dynamic model identification of industrial robots[J]. IEEE Transactions on Robotics, 36, 1577-1594(2020).

[10] DONG J W, XU J M, ZHOU Q Q et al. Dynamic identification of industrial robot based on nonlinear friction model and LS-SOS algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 70, 7504512(2833).

[11] PRESSE C, GAUTIER M. New criteria of exciting trajectories for robot identification[C], 907-912(1993).

[12] CALAFIORE G, INDRI M, BONA B. Robot dynamic calibration： optimal excitation trajectories and experimental parameter estimation[J]. Journal of Robotic Systems, 18, 55-68(2001).

[13] SWEVERS J, GANSEMAN C, TUKEL D B et al. Optimal robot excitation and identification[J]. IEEE Transactions on Robotics and Automation, 13, 730-740(1997).

[14] ATKESON C G, AN C H, HOLLERBACH J M. Estimation of inertial parameters of manipulator loads and links[J]. The International Journal of Robotics Research, 5, 101-119(1986).

[15] [15] 徐超. 关节型机器人的动力学参数辨识及前馈控制研究［D］. 南京： 东南大学， 2017.XUC. Research on Dynamic Parameter Identification and Feedforward Control of Articulated Robots［D］.Nanjing： Southeast University， 2017. （in Chinese）

[16] WU W X, ZHU S Q, WANG X Y et al. Closed-loop dynamic parameter identification of robot manipulators using modified Fourier series[J]. International Journal of Advanced Robotic Systems, 9, 29(2012).

[17] BONNET V, FRAISSE P, CROSNIER A et al. Optimal exciting dance for identifying inertial parameters of an anthropomorphic structure[J]. IEEE Transactions on Robotics： A Publication of the IEEE Robotics and Automation Society, 32, 823-836(2016).

[18] GAUTIER M. Dynamic identification of robots with power model[C], 1922-1927(2002).

[19] SWEVERS J, GANSEMAN C, TUKEL D B et al. Optimal robot excitation and identification[J]. IEEE Transactions on Robotics and Automation, 13, 730-740(1997).

[20] GAUTIER M, POIGNET P. Extended Kalman filtering and weighted least squares dynamic identification of robot[J]. Control Engineering Practice, 9, 1361-1372(2001).

[21] SOUSA C D, CORTESÃO R. Physical feasibility of robot base inertial parameter identification： a linear matrix inequality approach[J]. The International Journal of Robotics Research, 33, 931-944(2014).

[22] SOUSA C D, CORTESÃO R. Inertia tensor properties in robot dynamics identification： a linear matrix inequality approach[J]. IEEE/ASME Transactions on Mechatronics, 24, 406-411(2019).

[23] WENSING P M, KIM S, SLOTINE J J E. Linear matrix inequalities for physically consistent inertial parameter identification： a statistical perspective on the mass distribution[J]. IEEE Robotics and Automation Letters, 3, 60-67(2017).

[24] GIJSBERTS A, METTA G. Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression[J]. Neural Networks, 41, 59-69(2013).

[25] HU J, XIONG R. Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter[J]. IEEE Transactions on Industrial Electronics, 65, 3365-3375(2017).