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FAT-based robust adaptive control of cooperative multiple manipulators without velocity measurement

Published online by Cambridge University Press:  27 September 2021

Ali Deylami  and
Alireza Izadbakhsh Open the ORCID record for Alireza Izadbakhsh [Opens in a new window]
Ali Deylami
Affiliation:
Department of Electrical Engineering, Garmsar branch, Islamic Azad University, Garmsar, Iran
Alireza Izadbakhsh*
Affiliation:
Department of Electrical Engineering, Garmsar branch, Islamic Azad University, Garmsar, Iran
*
*Corresponding author. E-mail: [email protected]
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Abstract

This article addresses the problem of pose and force control in a cooperative system comprised of multiple n-degree-of-freedom (n-DOF) electrically driven robotic arms that move a payload. The proposed controller should be capable of maintaining the position and orientation of the payload in the desired path. In addition, the force exerted by robot end effectors on the object must remain limited. The system has unmodeled dynamics, and measuring the robot joint velocities is impossible. Therefore, a FAT-based observer–controller is designed to estimate the uncertainty and velocities based on universal approximation property of Fourier series expansion. The stability of the system is confirmed based on Lyapunov’s stability theorem. Finally, the proposed adaptive controller–observer is applied on two 3-DOF cooperative robotic arms carrying a payload, and the results are precisely analyzed. The results of the proposed approach are also compared with two state-of-art powerful approximation method.

Keywords

Adaptive controlCooperative manipulatorsFunction approximation techniqueUncertainties
Type
Research Article
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1732 - 1762
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Yousefizadeh, S. and Bak, T., “Unknown external force estimation and collision detection for a cooperative robot,” Robotica 38(9), 1665–1681 (2020). doi: 10.1017/S0263574719001681.CrossRefGoogle Scholar
Alevizos, K., Bechlioulis, C. and Kyriakopoulos, K., “Physical human–robot cooperation based on robust motion intention estimation,” Robotica 38(10), 1842–1866 (2020). doi: 10.1017/S0263574720000958.Google Scholar
Qi, X. and Cai, Z., “Cooperative pursuit control for multiple under actuated underwater vehicles with time delay in three-dimensional space,” Robotica 39(6), 1101–1115 (2021). doi: 10.1017/S0263574720000922.CrossRefGoogle Scholar
Hou, Z. and Zhang, G., “Fast convergence of multi-quadrotor cooperation using weighted-neighbor-based control,” Robotica, 1–18 (2021). doi: 10.1017/S0263574721000126.CrossRefGoogle Scholar
Miao, Z., Wang, Y. and Fierro, R., “Cooperative circumnavigation of a moving target with multiple nonholonomic robots using backstepping design,” Syst. Cont. Lett. 103, 58–65 (2017). doi: 10.1016/j.sysconle.2017.03.004.CrossRefGoogle Scholar
Pizetta, I. H. B., Brandão, A. S. and Sarcinelli-Filho, M., “Avoiding obstacles in cooperative load transportation,” ISA Trans. 91, 253–261 (2019). doi: 10.1016/j.isatra.2019.01.019.CrossRefGoogle Scholar
He, C., Huang, K., Chen, X., Zhang, Y. and Zhao, H., “Transportation control of cooperative double-wheel inverted pendulum robots adopting Udwadia-control approach,” Nonlinear Dyn. 91(4), 2789–2802 (2018). doi: 10.1007/s11071-018-4046-z.CrossRefGoogle Scholar
Zarafshan, P. and Moosavian, S. A. A., “Fuzzy tuning control approach to perform cooperative object manipulation by a rigid–flexible multibody robot,” Multibody Syst. Dyn. 40(3), 213–233 (2017). doi: 10.1007/s11044-017-9567-6.CrossRefGoogle Scholar
Sun, L., Huo, W. and Jiao, Z., “Robust nonlinear adaptive relative pose control for cooperative spacecraft during rendezvous and proximity operations,” IEEE Trans. Cont. Syst. Technol. 25(5), 1840–1847 (2016). doi: 10.1109/TCST.2016.2618907.Google Scholar
Wang, J. W., Guo, Y., Fahad, M. and Bingham, B., “Dynamic plume tracking by cooperative robots,” IEEE/ASME Trans. Mechatron. 24(2), 609–620 (2019). doi: 10.1109/TMECH.2019.2892292.CrossRefGoogle Scholar
Ortega, R., Perez, J. A. L., Nicklasson, P. J. and Sira-Ramirez, H. J., Passivity-Based Control of Euler-Lagrange Systems: Mechanical, Electrical and Electromechanical Applications (Springer Science & Business Media, 2013).Google Scholar
Zhang, Z., Song, Y. and Zhao, K., “Neuroadaptive cooperative control without velocity measurement for multiple humanoid robots under full-state constraints,” IEEE Trans. Ind. Electron. 66(4), 2956–2964 (2018). doi: 10.1109/TIE.2018.2844791.CrossRefGoogle Scholar
Li, X., Xu, Z., Li, S., Wu, H. and Zhou, X., “Cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network,” IEEE Access 8, 40029–40038 (2020). doi: 10.1109/ACCESS.2020.2974248.CrossRefGoogle Scholar
Tuan, D. M. and Hieu, P. D., “Adaptive position/force control for robot manipulators using force and velocity observer,” J. Electr. Eng. Technol. 14(6), 2575–2582 (2019). doi: 10.1007/s42835-019-00281-z.CrossRefGoogle Scholar
Yang, Z., Peng, J. and Liu, Y., “Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer,” Neurocomputing 331: 263–280 (2019). doi: 10.1016/j.neucom.2018.11.068.CrossRefGoogle Scholar
Shojaei, K., Kazemy, A. and Chatraei, A., “An observer-based neural adaptive PID2 controller for robot manipulators including motor dynamics with a prescribed performance,” IEEE/ASME Trans Mechatron (2020). doi: 10.1109/TMECH.2020.3028968.Google Scholar
Izadbakhsh, A., Khorashadizadeh, S. and Ghandali, S., “Robust adaptive impedance control of robot manipulators using Szász–Mirakyan operator as universal approximator,” ISA Trans. 106, 1–11 (2020). doi: 10.1016/j.isatra.2020.06.017.CrossRefGoogle ScholarPubMed
Izadbakhsh, A. and Masoumi, M., “FAT-Based Robust Adaptive Control of Flexible-Joint Robots: Singular Perturbation Approach,” In: 2017 IEEE International Conference on Industrial Technology (ICIT) (IEEE, 2017) pp. 803–808. doi: 10.1109/ICIT.2017.7915462.CrossRefGoogle Scholar
Izadbakhsh, A., Kheirkhahan, P. and Khorashadizadeh, S., “FAT-based robust adaptive control of electrically driven robots in interaction with environment,” Robotica 37(5), 779–800 (2019). doi: 10.1017/S0263574718001327.CrossRefGoogle Scholar
Izadbakhsh, A., Zamani, I. and Khorashadizadeh, S., “Szász–Mirakyan-based adaptive controller design for chaotic synchronization,” Int. J. Robust Nonlinear Cont. 31(5), 1689–1703 (2021). doi: 10.1002/rnc.5380.CrossRefGoogle Scholar
Izadbakhsh, A., “FAT-based robust adaptive control of electrically driven robots without velocity measurements,” Nonlinear Dyn. 89(1), 289–304 (2017). doi: 10.1007/s11071-017-3454-9.CrossRefGoogle Scholar
Chien, M. C. and Huang, A. C., “Design of a fat-based adaptive visual servoing for robots with time varying uncertainties,” Int. J. Optomechatron. 4(2), 93–114 (2010). doi: 10.1080/15599612.2010.484524.CrossRefGoogle Scholar
Jean, J.-H. and Fu, L.-C., “An adaptive control scheme for coordinated multi manipulator systems,” IEEE Trans. Robot Automat. 9, 226–231 (1993).CrossRefGoogle Scholar
Song, G. and Cai, L., “A Smooth Robust Control Approach to Cooperation of Multiple Robot Manipulators,” In: Proc. American Control Conf. Seattle, WA (1995) pp. 1382–1386.Google Scholar
Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control (Wiley, New York, 1989).Google Scholar
Woon, L. C., Ge, S. S., Chen, C. Q. and Zhang, C., “Adaptive neural network control of coordinated manipulators,” J. Robot. Syst. 16(4), 195–211 (1999).3.0.CO;2-R>CrossRefGoogle Scholar
Yao, B. and Tomizuka, M., “Adaptive Coordinated Control of Multiple Manipulators Handling a Constrained Object,” In: Proc. IEEE Int. Conf. Robot. Autom., Atlanta, GA (1993) pp. 624–629.Google Scholar
Huang, A. C. and Chen, M.-C., Adaptive Control of Robot Manipulators–A Unified Regressor Free Approach (World Scientific, Singapore, 2010).CrossRefGoogle Scholar
Arteaga, M. A. and Kelly, R., “Robot control without velocity measurements: New Theory and Experimental results.” IEEE Trans Robot. Automat. 20(2), 297–308 (2004).CrossRefGoogle Scholar
Uchiyama, M. and Dauchez, P., “A Symmetric Hybrid Position/Force Control Scheme for the Coordination of Two Robots, In: Proc. IEEE Int. Conf. Robot. Autom. (1988) pp. 350–356.Google Scholar
Baigzadehnoe, B., Rahmani, Z., Khosravi, A. and Rezaie, B., “On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach,” ISA Trans. 70, 432–446 (2017). doi: 10.1016/j.isatra.2017.07.029.CrossRefGoogle ScholarPubMed
Izadbakhsh, A., Khorashadizadeh, S. and Kheirkhahan, P., “Real-time fuzzy fractional-order control of electrically driven flexible-joint robots,” AUT J. Model. Simul. (2018). doi: 10.22060/MISCJ.2018.13523.5075.CrossRefGoogle Scholar
Liu, Z., Chen, C., Zhang, Y. and Chen, C. L. P., “Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism,” IEEE Trans. Cybern. 45(3), 521–532 (2015).Google ScholarPubMed