Optics and Precision Engineering, Volume. 32, Issue 2, 193(2024)
Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation
[1] J WU, J S WANG, Z YOU. An overview of dynamic parameter identification of robots. 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] Y LI, Y H LI, M C ZHU et al. A nonlinear momentum observer for sensorless robot collision detection under model uncertainties. 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] N DEHIO, J SMITH, D L WIGAND et al. Enabling impedance-based physical human–multi–robot collaboration: experiments with four torque-controlled manipulators. The International Journal of Robotics Research, 41, 68-84(2022).
[6] J JIN, N GANS. Parameter identification for industrial robots with a fast and robust trajectory design approach. Robotics and Computer Integrated Manufacturing, 31, 21-29(2015).
[7] M GAUTIER, W KHALIL. Direct calculation of minimum set of inertial parameters of serial robots. 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] Y HAN, J H WU, C LIU et al. An iterative approach for accurate dynamic model identification of industrial robots. IEEE Transactions on Robotics, 36, 1577-1594(2020).
[10] J W DONG, J M XU, Q Q ZHOU et al. Dynamic identification of industrial robot based on nonlinear friction model and LS-SOS algorithm. IEEE Transactions on Instrumentation and Measurement, 70, 7504512(2833).
[11] C PRESSE, M GAUTIER. New criteria of exciting trajectories for robot identification, 907-912(1993).
[12] G CALAFIORE, M INDRI, B BONA. Robot dynamic calibration: optimal excitation trajectories and experimental parameter estimation. Journal of Robotic Systems, 18, 55-68(2001).
[13] J SWEVERS, C GANSEMAN, D B TUKEL et al. Optimal robot excitation and identification. IEEE Transactions on Robotics and Automation, 13, 730-740(1997).
[14] C G ATKESON, C H AN, J M HOLLERBACH. Estimation of inertial parameters of manipulator loads and links. 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] W X WU, S Q ZHU, X Y WANG et al. Closed-loop dynamic parameter identification of robot manipulators using modified Fourier series. International Journal of Advanced Robotic Systems, 9, 29(2012).
[17] V BONNET, P FRAISSE, A CROSNIER et al. Optimal exciting dance for identifying inertial parameters of an anthropomorphic structure. IEEE Transactions on Robotics: A Publication of the IEEE Robotics and Automation Society, 32, 823-836(2016).
[18] M GAUTIER. Dynamic identification of robots with power model, 1922-1927(2002).
[19] J SWEVERS, C GANSEMAN, D B TUKEL et al. Optimal robot excitation and identification. IEEE Transactions on Robotics and Automation, 13, 730-740(1997).
[20] M GAUTIER, P POIGNET. Extended Kalman filtering and weighted least squares dynamic identification of robot. Control Engineering Practice, 9, 1361-1372(2001).
[21] C D SOUSA, R CORTESÃO. Physical feasibility of robot base inertial parameter identification: a linear matrix inequality approach. The International Journal of Robotics Research, 33, 931-944(2014).
[22] C D SOUSA, R CORTESÃO. Inertia tensor properties in robot dynamics identification: a linear matrix inequality approach. IEEE/ASME Transactions on Mechatronics, 24, 406-411(2019).
[23] P M WENSING, S KIM, J J E SLOTINE. Linear matrix inequalities for physically consistent inertial parameter identification: a statistical perspective on the mass distribution. IEEE Robotics and Automation Letters, 3, 60-67(2017).
[24] A GIJSBERTS, G METTA. Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression. Neural Networks, 41, 59-69(2013).
[25] J HU, R XIONG. Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter. IEEE Transactions on Industrial Electronics, 65, 3365-3375(2017).
Get Citation
Copy Citation Text
Yufei ZHOU, Zhongcan LI, Yi LI, Jingkai CUI, Shunfeng HE, Zhanyi SHENG, Mingchao ZHU. Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation[J]. Optics and Precision Engineering, 2024, 32(2): 193
Category:
Received: Jun. 2, 2023
Accepted: --
Published Online: Apr. 2, 2024
The Author Email: ZHU Mingchao (mingchaozhu@gmail.com)