Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-30T10:26:54.476Z Has data issue: false hasContentIssue false
  • English
  • Français

Vision-based adaptive LT sliding mode admittance control for collaborative robots with actuator saturation

Published online by Cambridge University Press:  09 May 2024

Cong Huang ,
Minglei Zhu Open the ORCID record for Minglei Zhu [Opens in a new window] ,
Shijie Song ,
Yuyang Zhao  and
Jinmao Jiang
Cong Huang
Affiliation:
Institue of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Minglei Zhu*
Affiliation:
Institue of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Shijie Song
Affiliation:
Institue of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Yuyang Zhao
Affiliation:
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Jinmao Jiang
Affiliation:
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
*
Corresponding author: Minglei Zhu; Email: Minglei.zhu@uestc.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

Keywords

collaborative robotsvision-admittance modeladaptive sliding mode controlinput saturation
Type
Research Article
Information
Robotica , First View , pp. 1 - 18
Copyright
© The Author(s), 2024. 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

Nabat, V., de la O Rodriguez, M., Company, O., Krut, S. and Pierrot, F., “Par4: Very High Speed Parallel Robot for Pick-and-Place,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems, (IEEE, 2005) pp. 553–558.CrossRefGoogle Scholar
Yang, G., Chen, I.-M., Yeo, S. H. and Lin, W.. Design and Analysis of a Modular Hybrid Parallel-Serial Manipulator for Robotised Deburring Applications (Springer London, London, 2008) pp. 167–188.Google Scholar
Xu, P., Li, B. and Chueng, C.-F., “Dynamic Analysis of a Linear Delta Robot in Hybrid Polishing Machine Based on the Principle of Virtual Work,” In: 18th International Conference on Advanced Robotics (ICAR) 2017, (IEEE, 2017) pp. 379–384.CrossRefGoogle Scholar
Wu, J., Gao, Y., Zhang, B. and Wang, L., “Workspace and dynamic performance evaluation of the parallel manipulators in a spray-painting equipment,” Robot Com-Int Manuf 44, 199–207 (2017).CrossRefGoogle Scholar
Oyman, E. L., Korkut, M. Y., Yilmaz, C., Bayraktaroglu, Z. Y. and Arslan, M. S., “Design and control of a cable-driven rehabilitation robot for upper and lower limbs,” Robotica 40(1), 1–37 (2022).CrossRefGoogle Scholar
Raibert, M. H. and Craig, J. J., “Hybrid position/Force control of manipulators,” J Dyn Syst Meas Cont 103(2), 126–133 (1981).CrossRefGoogle Scholar
Chiaverini, S. and Sciavicco, L., “The parallel approach to force/position control of robotic manipulators,” IEEE Trans Robot Autom 9(4), 361–373 (1993).CrossRefGoogle Scholar
Zhu, M., Huang, C., Qiu, Z., Zheng, W. and Gong, D., “Parallel image-based visual servoing/force control of a collaborative delta robot,” Front Neurorobotics 16 (2022). https://doi.org/10.3389/fnbot.2022.922704 CrossRefGoogle ScholarPubMed
Mason, M. T., “Compliance and force control for computer controlled manipulators,” IEEE Trans Syst Man Cyber 11(6), 418–432 (1981).CrossRefGoogle Scholar
Liu, C. and Li, Z., “Force tracking smooth adaptive admittance control in unknown environment,” Robotica 41(7), 1991–2011 (2023).CrossRefGoogle Scholar
Huang, A.-C., Lee, K.-J. and Du, W.-L., “Contact force cancelation in robot impedance control by target impedance modification,” Robotica 41(6), 1733–1748 (2023).CrossRefGoogle Scholar
Qi, R., Tang, Y. and Zhang, K., “An optimal visual servo trajectory planning method for manipulators based on system nondeterministic model,” Robotica 40(6), 1665–1681 (2022).CrossRefGoogle Scholar
De Schutter, J. and Baeten, J.. Integrated Visual Servoing and Force Control: The Task Frame Approach (Springer Science & Business Media, Berlin, Heidelberg, 2003).Google Scholar
Bellakehal, S., Andreff, N., Mezouar, Y. and Tadjine, M., “Vision/force control of parallel robots,” Mech Mach Theory 46(10), 1376–1395 (2011).CrossRefGoogle Scholar
Zhou, Y., Li, X., Yue, L., Gui, L., Sun, G., Jiang, X. and Liu, Y.-H., “Global Vision-Based Impedance Control for Robotic Wall Polishing,” In: IEEE/RSJ international conference on intelligent robots and systems (IROS) 2019, (IEEE, 2019) pp. 6022–6027.CrossRefGoogle Scholar
Lippiello, V., Fontanelli, G. A. and Ruggiero, F., “Image-based visual-impedance control of a dual-arm aerial manipulator,” IEEE Robot Auto Lett 3(3), 1856–1863 (2018).CrossRefGoogle Scholar
Odira, J. K., Andreff, N., Petiet, L. and Byiringiro, J. B., “Visual servoing of a laser beam through a mirror,” Robotica 40(9), 3157–3177 (2022).CrossRefGoogle Scholar
Hu, Q., “Adaptive output feedback sliding-mode manoeuvring and vibration control of flexible spacecraft with input saturation,” IET Cont Theory Appl 2(6), 467–478 (2008).CrossRefGoogle Scholar
He, W., Dong, Y. and Sun, C., “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Trans Syst Man Cybernet: Syst 46(3), 334–344 (2015).CrossRefGoogle Scholar
Zhou, Q., Zhao, S., Li, H., Lu, R. and Wu, C., “Adaptive neural network tracking control for robotic manipulators with dead zone,” IEEE Trans Neur Net Lear Syst 30(12), 3611–3620 (2019).CrossRefGoogle ScholarPubMed
Dai, X., Song, S., Xu, W., Huang, Z. and Gong, D., “Modal space neural network compensation control for gough-stewart robot with uncertain load - sciencedirect,” Neurocomputing 449, 245–257 (2021).CrossRefGoogle Scholar
Haninger, K., Hegeler, C. and Peternel, L., “Model Predictive Control with Gaussian Processes for Flexible Multi-Modal Physical Human Robot Interaction,” In: International Conference on Robotics and Automation (ICRA) 2022, (IEEE, 2022) pp. 6948–6955.CrossRefGoogle Scholar
Ahmadi, B., Xie, W.-F. and Zakeri, E., “Robust cascade vision/force control of industrial robots utilizing continuous integral sliding-mode control method,” IEEE/ASME Trans Mechatr 27(1), 524–536 (2021).CrossRefGoogle Scholar
Qian, S., Zhao, Z., Qian, P., Wang, Z. and Zi, B., “Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots,” Robotica 42(1), 1–20 (2024).CrossRefGoogle Scholar
Cheng, J., Zhang, X., Sun, T. and Yang, H., “Robust Impedance Control for a Five-Bar Parallel Robot Based on Uncertainty Estimation,” In: International Symposium on Autonomous Systems (ISAS) 2020, (IEEE, 2020) pp. 137–141.CrossRefGoogle Scholar
Tuan, L. A. and Ha, Q. P., “Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots,” Robotica, 1–24 (2024).Google Scholar
Jia, S. and Shan, J., “Finite-time trajectory tracking control of space manipulator under actuator saturation,” IEEE Trans Ind Electron 67(3), 2086–2096 (2019).CrossRefGoogle Scholar
Fu, X., Ai, H. and Chen, L., “Integrated sliding mode control with input restriction, output feedback and repetitive learning for space robot with flexible-base, flexible-link and flexible-joint,” Robotica 41(1), 370–391 (2023).CrossRefGoogle Scholar
Plestan, F., Shtessel, Y., Bregeault, V. and Poznyak, A., “New methodologies for adaptive sliding mode control,” Int J Control 83(9), 1907–1919 (2010).CrossRefGoogle Scholar
Baek, J., Jin, M. and Han, S., “A new adaptive sliding-mode control scheme for application to robot manipulators,” IEEE Trans Ind Electron 63(6), 3628–3637 (2016).CrossRefGoogle Scholar
Shao, K., Zheng, J., Wang, H., Wang, X. and Liang, B., “Leakage-type adaptive state and disturbance observers for uncertain nonlinear systems,” Nonlinear Dynam 105(3), 2299–2311 (2021).CrossRefGoogle Scholar
Roy, S., Roy, S. B. and Kar, I. N., “Adaptive-robust control of euler-lagrange systems with linearly parametrizable uncertainty bound,” IEEE Trans Contr Syst Tech 26(5), 1842–1850 (2017).CrossRefGoogle Scholar
Zhu, M., Briot, S. and Chriette, A., “Sensor-based design of a delta parallel robot,” Mechatronics 87, 102893 (2022).CrossRefGoogle Scholar
Mariottini, G. L., Oriolo, G. and Prattichizzo, D., “Image-based visual servoing for nonholonomic mobile robots using epipolar geometry,” IEEE Trans Robot 23(1), 87–100 (2007).CrossRefGoogle Scholar
Chaumette, F. and Hutchinson, S., “Visual servo control. i. basic approaches,” IEEE Robot Autom Mag 13(4), 82–90 (2006).CrossRefGoogle Scholar
Zhang, H. and Ostrowski, J. P., “Visual Servoing with Dynamics: Control of an Unmanned Blimp,” In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat No. 99CH36288C), (IEEE, 1999) pp. 618–623.Google Scholar
Fusco, F., Kermorgant, O. and Martinet, P., “A Comparison of Visual Servoing from Features Velocity and Acceleration Interaction Models,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019, (IEEE, 2019) pp. 4447–4452.CrossRefGoogle Scholar
Ozawa, R. and Chaumette, F., “Dynamic Visual Servoing with Image Moments for a Quadrotor Using a Virtual Spring Approach,” In: IEEE International Conference on Robotics and Automation 2011, (IEEE, 2011) pp. 5670–5676.CrossRefGoogle Scholar
Wang, F., Liu, Z., Chen, C. L. P. and Zhang, Y., “Adaptive neural network-based visual servoing control for manipulator with unknown output nonlinearities,” Inform Sci 451-452, 16–33 (2018).CrossRefGoogle Scholar
Merlet, J. P.. Parallel Robots (Springer, Netherlands, (2006).Google Scholar
Briot, S. and Martinet, P., “Minimal Representation for the Control of Gough-Stewart Platforms via Leg Observation Considering a Hidden Robot Model,” In: IEEE international conference on robotics and automation 2013, (IEEE, 2013) pp. 4653–4658.CrossRefGoogle Scholar
Roy, S., Baldi, S. and Fridman, L. M., “On adaptive sliding mode control without a priori bounded uncertainty,” Automatica 111, 108650 (2020).CrossRefGoogle Scholar
Karason, S. P. and Annaswamy, A. M., “Adaptive control in the presence of input constraints,” In: 1993 American control conference, (IEEE, 1993) pp. 1370–1374.CrossRefGoogle Scholar
Slotine, J.-J. E. and Li, W.. Applied nonlinear control (Prentice hall, Englewood Cliffs, NJ, 1991).Google Scholar
Gao, J., An, X., Proctor, A. and Bradley, C., “Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles,” Ocean Eng 142, 666–675 (2017).CrossRefGoogle Scholar