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Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

Published online by Cambridge University Press:  07 January 2022

Peng Zhang  and
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]
Peng Zhang
Affiliation:
Tianjin University of Science and Technology, Tianjin 300222, China Tianjin Key Laboratory of Integrated Design and Online Monitoring of Light Industry & Food Engineering Machinery and Equipment, Tianjin 300222, China
Junxia Zhang*
Affiliation:
Tianjin University of Science and Technology, Tianjin 300222, China Tianjin Key Laboratory of Integrated Design and Online Monitoring of Light Industry & Food Engineering Machinery and Equipment, Tianjin 300222, China
*
*Corresponding author: Junxia Zhang. E-mail: zjx@tust.edu.cn
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Abstract

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

Keywords

parameters identificationexoskeleton robotbeetle swarm optimization algorithm
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2716 - 2731
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Brahmi, B., Saad, M., Rahman, M. H. and Ochoa-Luna, C., “Cartesian trajectory tracking of a 7-DOF exoskeleton robot based on human inverse kinematics,” IEEE Trans. Syst. Man Cybern. Syst. 49(3), 600–611 (2019).Google Scholar
Li, Z., Zhao, T., Chen, F., Hu, Y., Su, C. and Fukuda, T., “Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoid like mobile manipulator,” IEEE/ASME Trans. Mechatron. 23(1), 121–131 (2018).CrossRefGoogle Scholar
Viteckova, S., Kutilek, P., De Boisboissel, G., Krupicka, R., Galajdova, A., Kauler, J. and Szabo, Z., “Empowering lower limbs exoskeletons: State-of-the-art,” Robotica 36(11), 1743–1756 (2018).CrossRefGoogle Scholar
Lee, K., Liu, D., Perroud, L., Chavarriaga, R. and Millana, J. R., “A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers,” Robot. Auton. Syst. 90(48), 15–23 (2017).CrossRefGoogle Scholar
Anwar, T. and Al Jumaily, A., “System Identification and Damping Coefficient Estimation from EMG Based on ANFIS to Optimize Human Exoskeleton Interaction,” 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada (IEEE, 2016).Google Scholar
Mallat, R., Bonnet, V., Huo, W., Karasinski, P., Amirat, Y., Khalil, M. and Mohammed, S., “Human-Exoskeleton System Dynamics Identification Using Affordable Sensors,” 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia (IEEE, 2018).Google Scholar
Jianxiong, L., Research on Torque Compensation Control of Industrial Robot Based on Dynamics Model. Jiangnan University, Wuxi, China (2019).Google Scholar
Jina, Y., Tsai, F. T.-C. and Kao, S.-C., “Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach,” J. Hydrol. 601 (2021). doi: 10.1016/j.jhydrol.2021.126682.Google Scholar
Swevers, J., Verdonck, W., Naumer, B. and Pieters, S., “An experimental robot load identification method for industrial application,” Int. J. Rob. Res. 8(21), 701–712 (2002).CrossRefGoogle Scholar
Kinsheel, A. and Taha, Z., “Robust least square estimation of the CRS A465 robot arm’s dynamic model parameters,” J. Mech. Eng. Res. 4(3), 89–99 (2012).Google Scholar
Jonker, J., Zheng, P. and Aravkin, A. Y., “Efficient robust parameter identification in generalized kalman smoothing models,” IEEE Trans. Autom. Control 66(10), 4852–4857 (2021).CrossRefGoogle Scholar
Gautier, M. and Khalil, W., “Direct calculation of minimum set of inertial parameters of serial robots,” IEEE Trans. Rob. Autom. 6(3), 369–373 (1990).CrossRefGoogle Scholar
Gautier, M., “Numerical Calculation of the Base Parameters of Robots,” IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA (1990) pp. 1020–1025.Google Scholar
Jing, C., Yuping, H., Xibin, G., Shit, Z. ong, and Longfei, J., “Parameter identification and adaptive compliant control of rehabilitation exoskeleton based on multiple sensors,” Measurement 159(7), 107765, (2021).Google Scholar
Park, K.-J., “Fourier-based optimal excitation trajectories for the dynamic identification of robots,” Robotica 24(5), 625–633 (2006).CrossRefGoogle Scholar
Zhang, T., Liang, X. and Qin, B.-B., “Dynamic parameter identification of SCARA robot based on Newton Euler method,” J. South China Univ. Technol. (Natural Science Edition) 45(10), 129–136 (2017).Google Scholar
Zhiyu, W., Bin, W. and Chaohui, W., “Parameter identification of supercapacitor equivalent circuit model using nonlinear least square method,” J. Xian Jiaotong Univ. 54(4), 10–18 (2020).Google Scholar
Li, D., Hongtao, W. and Yu, Y., “Parameter identification of industrial robots based on WLS-ABC algorithm,” J. South China Univ. Technol. (Natural Sci. Edition) 44(5), 90–95 (2016).Google Scholar
Ghan, J., Steger, R. and Kazerooni, H., “Control and System Identification for the Berkeley Lower Extremity Exoskeleton (BLEEX),” Proceedings - IEEE International Conference on Robotics and Automation, Orlando, FL, USA (2006) pp. 989–1014.Google Scholar
Pei, P., Pei, Z., Gu, H. and Tang, Z., “Dynamics Compensation Strategy for Control of Lower Extremity Exoskeleton,” Proceedings of The IEEE 2019 9th International Conference On Cybernetics And Intelligent Systems (CIS) Robotics, Automation and Mechatronics (RAM) (CIS & RAM 2019), Bangkok, Thailand (2019) pp. 1–6.Google Scholar
Bargsten, V., De Gea Fernandez, J. and Kassahun, Y., “Experimental Robot Inverse Dynamics Identification Using Classical and Machine Learning Techniques,” Proceedings of the 47th International Symposium on Robotics, Munich, Germany (2016).Google Scholar
Liu, N., L. Li and B. HAO, “Modeling and simulation of robot inverse dynamics using LSTM-based deep learning algorithm for smart cities and factories,” IEEE Access 7(38), 173989–173998 (2019).CrossRefGoogle Scholar
Gong, Z., Zheng, X., Zhicheng, H. and Wenlin, Y., “A systematic error compensation strategy based on an optimized recurrent neural network for collaborative robot dynamics,” Appl. Sci. Basel 10(19) (2020). doi: 10.3390/app10196743.Google Scholar
Enwei, C., Research on robot dynamic characteristics and dynamic parameter identification. Hefei University of Technology, Anhui, China (2016).Google Scholar
Rueckert, E., Nakatenus, M. and Tosatto, S., “Learning Inverse Dynamics Models in on Time with LSTM Networks,” Proceedings of the 17th IEEE-RAS International Conference on Humanoid Robotics, Birmingham, UK (2017).Google Scholar
Verdel, D., Bastide, S., Vignais, N., Bruneau, O. and Berret, B., “An identification-based method improving the transparency of a robotic upper limb exoskeleton,” Robotica 39(9), 1711–1728 (2021). doi: 10.1017/S0263574720001459.CrossRefGoogle Scholar
Chang, X., An, H. and Ma, H., “Modeling and base parameters identification of legged robots,” Robotica, 1–15 (2021). doi: 10.1017/S0263574721000783.CrossRefGoogle Scholar
Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, WA, Australia (1995) pp. 1942–1948.CrossRefGoogle Scholar
Ye, M. and Wang, X., “Parameter estimation of the Bouc-Wen hysteresis model using particle swarm optimization,” Smart Mater. Struct. 16(6), 2341 (2017).CrossRefGoogle Scholar
Yueling, W., Yue, W. and Qi, W., “Dynamic parameter identification of flexible joint robot based on adaptive particle swarm optimization genetic algorithm,” J. Meas. 41(1), 60–66 (2020).Google Scholar
Wentao, S., Parameter Identification Method of Exoskeleton Single Leg Based on Characteristic Analysis of Man-Machine Coupling System. Haerbin, Harbin Institute of Technology California, Haerbin, China (2017).Google Scholar
Qin, Z., Baron, L. and Birglen, L., “A new approach to the dynamic parameter identification of robotic manipulators,” Robotica 28(4), 539–547 (2010).CrossRefGoogle Scholar
Fusheng, Z., Wentao, S., Wei, G. and Shiyin, Q., “Dynamic parameter identification of a lower extremity exoskeleton using RLS-PSO,” Appl. Sci. Basel 9(2) (2019). doi: 10.3390/app9020324.Google Scholar
Goher Khaled, M. and Fadlallah Sulaiman, O., “Design, modeling, and control of a portable leg rehabilitation system,” J. Dyn. Syst. Meas. Control Trans. ASME 139(7) (2017). doi: 10.1115/1.4035815.Google Scholar
Xiangyuan, J. and Shuai, L., BAS: Beetle antenna search algorithm for optimization problems. Available: https://arxiv.org/pdf/1710.10724.pdf (2019).Google Scholar
Jiang, X. Y. and LI, S., “Beetle antennae search without parameter tuning (BAS-WPT) for multi-objective optimization,” Neural and Evolutionary Computing. Available: arXiv:1711.02395 (2019).Google Scholar
Liu, Y., Qian, Z. and Jia, D., “Universal localization algorithm based on beetle antennae search in indoor environment,” J. Electron. Inf. Technol. 41(7), 1565–1571 (2019).Google Scholar
Wu, Q., Ma, Z., Xu, G., Li, S. and Chen, D., “A novel neural network classifier using beetle antennae search algorithm for pattern classification,” IEEE Access 7(53), 64686–64696 (2019).CrossRefGoogle Scholar