Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-11T01:03:56.782Z Has data issue: false hasContentIssue false
  • English
  • Français

Hybrid impedance and admittance control for optimal robot–environment interaction

Published online by Cambridge University Press:  30 November 2023

Dexi Ye Open the ORCID record for Dexi Ye [Opens in a new window] ,
Chenguang Yang Open the ORCID record for Chenguang Yang [Opens in a new window] ,
Yiming Jiang  and
Hui Zhang
Dexi Ye
Affiliation:
Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
Chenguang Yang*
Affiliation:
Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
Yiming Jiang
Affiliation:
School of Robotics and the Visual Perception and Control Technology National Engineering Laboratory, Hunan University, Changsha, 410082, China
Hui Zhang
Affiliation:
School of Robotics and the Visual Perception and Control Technology National Engineering Laboratory, Hunan University, Changsha, 410082, China
*
Corresponding author: Chenguang Yang; Email: cyang@ieee.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Compliant interaction between robots and the environment is crucial for completing contact-rich tasks. However, obtaining and implementing optimal interaction behavior in complex unknown environments remains a challenge. This article develops a hybrid impedance and admittance control (HIAC) scheme for robots subjected to the second-order unknown environment. To obtain the second-order target impedance model that represents the optimal interaction behavior without the accurate environment dynamics and acceleration feedback, an impedance adaptation method with virtual inertia is proposed. Since impedance control and admittance control have complementary structures and result in unsatisfactory performance in a wide range of environmental stiffness due to their fixed causality, a hybrid system framework suitable for the second-order environment is proposed to generate a series of intermediate controllers which interpolate between the responses of impedance and admittance controls by using a switching controller and adjusting its switching duty cycle. In addition, the optimal intermediate controller is selected using a mapping of the optimal duty cycle to provide the optimal implementation performance for the target impedance model. The proposed HIAC scheme can achieve the desired interaction and impedance implementation performance while ensuring system stability. Simulation and experimental studies are performed to verify the effectiveness of our scheme with a 2-DOF manipulator and a 7-DOF Franka EMIKA panda robot, respectively.

Keywords

impedance controlimpedance adaptationadmittance controloptimal controlhybrid system
Type
Research Article
Information
Robotica , Volume 42 , Issue 2 , February 2024 , pp. 510 - 535
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Jiang, J., Yao, L., Huang, Z., Yu, G., Wang, L. and Bi, Z., “The state of the art of search strategies in robotic assembly,” J. Ind. Inf. Integr. 26, 100259 (2022).Google Scholar
Mukherjee, D., Gupta, K., Chang, L. H. and Najjaran, H., “A survey of robot learning strategies for human-robot collaboration in industrial settings,” Robot. Comput. Integr. Manuf. 73, 102231 (2022).10.1016/j.rcim.2021.102231CrossRefGoogle Scholar
Raibert, M. H. and Craig, J. J., “Hybrid position/force control of manipulators,” J. Dyn. Syst. Meas. Control 103(2), 126–133 (1981).10.1115/1.3139652CrossRefGoogle Scholar
Hogan, N., “Impedance Control: An Approach to Manipulation,” In: 1984 American Control Conference (1984) pp. 304–313.Google Scholar
Song, P., Yu, Y. and Zhang, X., “A tutorial survey and comparison of impedance control on robotic manipulation,” Robotica 37(5), 801–836 (2019).10.1017/S0263574718001339CrossRefGoogle Scholar
Colgate, J. E. and Hogan, N., “Robust control of dynamically interacting systems,” Int. J. Control 48(1), 65–88 (1988).10.1080/00207178808906161CrossRefGoogle Scholar
Buerger, S. P. and Hogan, N., “Complementary stability and loop shaping for improved human–robot interaction,” IEEE Trans. Robot. 23(2), 232–244 (2007).10.1109/TRO.2007.892229CrossRefGoogle Scholar
Izadbakhsh, A. and Khorashadizadeh, S., “Polynomial-based robust adaptive impedance control of electrically driven robots,” Robotica 39(7), 1181–1201 (2021).10.1017/S0263574720001009CrossRefGoogle Scholar
Izadbakhsh, A., Deylami, A. and Khorashadizadeh, S., “Superiority of q-Chlodowsky operators versus fuzzy systems and neural networks: Application to adaptive impedance control of electrical manipulators,” Expert Syst. Appl. 209, 118249 (2022).10.1016/j.eswa.2022.118249CrossRefGoogle Scholar
Sharkawy, A.-N. and Koustoumpardis, P. N., “Human–robot interaction: A review and analysis on variable admittance control, safety, and perspectives,” Machines 10(7), 591 (2022).10.3390/machines10070591CrossRefGoogle Scholar
Jin, Z., Qin, D., Liu, A., Zhang, W.-a. and Yu, L., “Model predictive variable impedance control of manipulators for adaptive precision-compliance tradeoff,” IEEE/ASME Trans. Mechatron. 28(2), 1174–1186 (2022).10.1109/TMECH.2022.3204350CrossRefGoogle Scholar
Sharkawy, A.-N., Koustoumpardis, P. N. and Aspragathos, N., “A neural network-based approach for variable admittance control in human–robot cooperation: Online adjustment of the virtual inertia,” Intell. Serv. Robot. 13(4), 495–519 (2020).10.1007/s11370-020-00337-4CrossRefGoogle Scholar
Roveda, L., Riva, D., Bucca, G. and Piga, D., “Sensorless optimal switching impact/force controller,” IEEE Access 9, 158167–158184 (2021).10.1109/ACCESS.2021.3131390CrossRefGoogle Scholar
Roveda, L., Shahid, A. A., Iannacci, N. and Piga, D., “Sensorless optimal interaction control exploiting environment stiffness estimation,” IEEE Trans. Control Syst. Technol. 30(1), 218–233 (2022).10.1109/TCST.2021.3061091CrossRefGoogle Scholar
Roveda, L., Testa, A., Shahid, A. A., Braghin, F. and Piga, D., “Q-Learning-based model predictive variable impedance control for physical human-robot collaboration,” Artif. Intell. 312, 103771 (2022).10.1016/j.artint.2022.103771CrossRefGoogle Scholar
Li, Y. and Ge, S. S., “Impedance learning for robots interacting with unknown environments,” IEEE Trans. Control Syst. Technol. 22(4), 1422–1432 (2014).10.1109/TCST.2013.2286194CrossRefGoogle Scholar
Peng, G., Chen, C. L. P. and Yang, C., “Robust admittance control of optimized robot-environment interaction using reference adaptation,” IEEE Trans. Neural Netw. Learn. Syst. 34(9), 5804–5815 (2022).10.1109/TNNLS.2021.3131261CrossRefGoogle Scholar
Zeng, C., Yang, C. and Chen, Z., “Bio-inspired robotic impedance adaptation for human-robot collaborative tasks,” Sci. China Inf. Sci. 63(7), 170201 (2020).10.1007/s11432-019-2748-xCrossRefGoogle Scholar
Qiao, H., Zhong, S., Chen, Z. and Wang, H., “Improving performance of robots using human-inspired approaches: A survey,” Sci. China Inf. Sci. 65(12), 221201 (2022).10.1007/s11432-022-3606-1CrossRefGoogle Scholar
Qiao, H., Wu, Y.-X., Zhong, S.-L., Yin, P.-J. and Chen, J.-H., “Brain-inspired intelligent robotics: Theoretical analysis and systematic application,” Mach. Intell. Res. 20(1), 1–18 (2023).10.1007/s11633-022-1390-8CrossRefGoogle Scholar
Lewis, F. L., Vrabie, D. and Syrmos, V. L., Optimal Control (John Wiley & Sons, New York, 2012).10.1002/9781118122631CrossRefGoogle Scholar
Wang, F.-Y., Zhang, H. and Liu, D., “Adaptive dynamic programming: An introduction,” IEEE Comput. Intell. Mag. 4(2), 39–47 (2009).10.1109/MCI.2009.932261CrossRefGoogle Scholar
Jiang, Y. and Jiang, Z.-P., “Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics,” Automatica 48(10), 2699–2704 (2012).CrossRefGoogle Scholar
Ge, S. S., Li, Y. and Wang, C., “Impedance adaptation for optimal robot–environment interaction,” Int. J. Control 87(2), 249–263 (2014).10.1080/00207179.2013.827799CrossRefGoogle Scholar
Huang, H., Yang, C. and Chen, C. L. P., “Optimal robot–environment interaction under broad fuzzy neural adaptive control,” IEEE Trans. Cybern. 51(7), 3824–3835 (2021).10.1109/TCYB.2020.2998984CrossRefGoogle ScholarPubMed
Yang, C., Peng, G., Li, Y., Cui, R., Cheng, L. and Li, Z., “Neural networks enhanced adaptive admittance control of optimized robot–environment interaction,” IEEE Trans. Cybern. 49(7), 2568–2579 (2019).10.1109/TCYB.2018.2828654CrossRefGoogle ScholarPubMed
Valency, T. and Zacksenhouse, M., “Accuracy/robustness dilemma in impedance control,” J. Dyn. Syst. Meas. Control 125(3), 310–319 (2003).10.1115/1.1590685CrossRefGoogle Scholar
Ott, C., Mukherjee, R. and Nakamura, Y., “Unified Impedance and Admittance Control,” In: 2010 IEEE International Conference on Robotics and Automation (IEEE, Anchorage, AK, 2010) pp. 554–561.10.1109/ROBOT.2010.5509861CrossRefGoogle Scholar
Ott, C., Mukherjee, R. and Nakamura, Y., “A hybrid system framework for unified impedance and admittance control,” J. Intell. Robot. Syst. 78(3-4), 359–375 (2015).10.1007/s10846-014-0082-1CrossRefGoogle Scholar
Formenti, A., Bucca, G., Shahid, A. A., Piga, D. and Roveda, L., “Improved impedance/admittance switching controller for the interaction with a variable stiffness environment,” Complex Eng. Syst. 2(3), 12 (2022).10.20517/ces.2022.16CrossRefGoogle Scholar
Rhee, I., Kang, G., Moon, S. J., Choi, Y. S. and Choi, H. R., “Hybrid impedance and admittance control of robot manipulator with unknown environment,” Intell. Serv. Robot. 16, 49–60 (2022).Google Scholar
Cavenago, F., Voli, L. and Massari, M., “Adaptive hybrid system framework for unified impedance and admittance control,” J. Intell. Robot. Syst. 91(3-4), 569–581 (2018).10.1007/s10846-017-0732-1CrossRefGoogle Scholar
Slotine, J.-J. E. and Li, W., Applied Nonlinear Control (Prentice Hall, Englewood Cliffs, NJ, 1991).Google Scholar
Das, T. and Mukherjee, R., “Shared-sensing and control using reversible transducers,” IEEE Trans. Control Syst. Technol. 17(1), 242–248 (2009).10.1109/TCST.2008.924570CrossRefGoogle Scholar