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Remote monitoring and control of the 2-DoF robotic manipulators over the internet

Published online by Cambridge University Press:  10 August 2022

Sadra Hokmi ,
Shahab Haghi  and
Alireza Farhadi Open the ORCID record for Alireza Farhadi [Opens in a new window]
Sadra Hokmi
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Shahab Haghi
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Alireza Farhadi*
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Abstract

This article is concerned with remote monitoring and control of the 2-degrees of freedom (DoF) robotic manipulators, which have nonlinear dynamics over the packet erasure channel, which is an abstract model for communication over the Internet, WiFi, or Zigbee modules. This type of communication is subject to imperfections, such as random packet dropout and rate distortion. These imperfections cause a significant challenge for monitoring and control of robotic manipulators in the industrial environments because sensitive data, such as sensor data and control commands may not ever reach to their destination resulting in significant performance degradation. Therefore, the effects of these imperfections must be compensated. In this article, we apply two coding and control techniques previously developed for the telepresence ad teleoperation of autonomous vehicles to compensate the effects of the above communication imperfections for remote monitoring and control of the 2-DoF robotic manipulators controlled over the packet erasure channel. To achieve this goal, we design a new linear controller and a new nonlinear controller for the 2-DoF robotic manipulators over the packet erasure channel. The first technique is based on the linearization method and the second technique uses a nonlinear controller. The performances of these two techniques for remote monitoring and control of robotic manipulators are evaluated and compared with each other in this paper. We illustrate their satisfactory performances in the presence of severe communication imperfections.

Keywords

telepresenceteleoperationrobotic manipulatorIndustrial Internet of Things (IIoT), the Industry 4.0
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4475 - 4497
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Hokayem, P. F. and Spong, M. W., “Bilateral teleoperation: A historical survey,” Automatica 42(12), 2035–2057 (2006).CrossRefGoogle Scholar
Nuno, E., Sarras, I. and Basanez, L., “An adaptive controller for bilateral teleoperation: Variable time-delay case,” IFAC Proc. 47(3), 9341–9346 (2014).CrossRefGoogle Scholar
Liu, C., Guo, J. and Poignet, P., “Nonlinear model - mediated teleoperation for surgical applications under time variant communication delay,” IFAC Proc. 51(22), 493–499 (2018).Google Scholar
Yoshida, K. and Namerikawa, T., “Predictive PD control for teleoperation with communication delay,” IFAC Proc. 41(2), 12703–12708 (2008).CrossRefGoogle Scholar
Eusebi, A. and Melchiorri, C., “Force reflecting telemanipulators with time-delay: Stability analysis and control design,” IEEE Trans. Robot. Autom. 14(4), 635–640 (1998).CrossRefGoogle Scholar
Imaida, T., Yokokohji, Y., Oda, T. M. and Yoshikwa, T., “Groundspace bilateral teleoperation of ETS-VII robot arm by direct bilateral coupling under 7-s time delay condition,” IEEE Trans. Robot. Autom. 20(3), 499–511 (2004).CrossRefGoogle Scholar
Anderson, R. J. and Spong, M. W., “Bilateral Control of Teleoperators with Time Delay,” In: Proceedings of the IEEE Conference on Decision and Control (1998) pp. 167–173.Google Scholar
Bejczy, A. K. and Kim, W. S., “Predictive Displays and Shared Compliance Control for Time-Delayed Telemanipulation,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1990) pp. 407–412.Google Scholar
Bemporad, A., “Predictive Control of Teleoperated Constrained Systems with Unbounded Communication Delays,” In: Proceedings of the IEEE Conference on Decision and Control , vol. 2 (1998) pp. 2133–2138.Google Scholar
Benedetti, C., Franchini, M. and Fiorini, P., “Stable Tracking in Variable Time-Delay Teleoperation,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , vol. 4 (2001) pp. 2252–2257.Google Scholar
Berestesky, P., Chopra, N. and Spong, M. W., “Discrete Time Passivity in Bilateral Teleoperation Over the Internet,” In: Proceedings of the IEEE International Conference on Robotics and Automation (2004).CrossRefGoogle Scholar
Dolgov, M., Fischer, J. and Hanebeck, U. D., “Event-based LQG control over networks with random transmission delays and packet losses,” IFAC Proc. 46(27), 23–30 (2013).CrossRefGoogle Scholar
Shah, D. and Mehta, A., “Discrete-time sliding mode controller subject to real-time fractional delays and packet losses for networked control system,” Int. J. Cont. Automat. Syst. 15(6), 2690–2703 (2017).CrossRefGoogle Scholar
Sun, Y., Sun, Y. and Yang, C., “Finite-time control of networked control systems with time delay and packet dropout,” J. Cont. Sci. Eng. 2021(2), 1–7 (2021).CrossRefGoogle Scholar
Tatikonda, S. and Mitter, S., “Control over noisy channels,” IEEE Trans. Automat. Cont. 49(7), 1196–1201 (2004).CrossRefGoogle Scholar
Parsa, A. and Farhadi, A., “Measurement and control of nonlinear dynamic systems over the internet (IoT): Applications in remote control of autonomous vehicles,” Automatica 95(12), 93–103 (2018).CrossRefGoogle Scholar
Parsa, A. Farhadi, and A., “New coding scheme for the state estimation and reference tracking of nonlinear dynamic systems over the packet erasure channel (IoT): Applications in tele-operation of autonomous vehicles,” Eur. J. Cont. 57(4), 242–252 (2021).CrossRefGoogle Scholar
Ustundag, A. and Cevikcan, E.. Industry 4.0: Managing The Digital Transformation (Springer, Cham, Switzerland, 2017).Google Scholar
Vishakha and Jain, S., “Big data analytics as a solution for threat analysis and risk mitigation in smart cities: A review,” Int. Res. J. 6(1), 1–5 (2017).Google Scholar
Slotine, J. J. and Li, W.. Applied Nonlinear Control (Prentice-Hall, Englewood Cliffs, NJ, 1991).Google Scholar
Ogata, K.. Discrete - Time Control Systems (Prentice Hall, Englewood Cliffs, NJ, 1987).Google Scholar
Chen, C., Zhang, C., Hu, T., Ni, H. and Chen, Q., “Finite-time tracking control for uncertain robotic manipulators using backstepping method and novel extended state observer,” Int. J. Adv. Robot. Syst. 16(3), 172988141984465 (May–June 2019).CrossRefGoogle Scholar
Kirk, D. E.. Introduction to Optimal Control Theory: An Introduction. Illustrated edition (Dover Publications, 26 April 2012).Google Scholar
Lin, H. and Antsaklis, P. J., “Stability and stabilizability of switched linear systems: A survey of recent results,” IEEE Trans. Automat. Contr. 54(2), 308–322 (2009).Google Scholar
Zhai, G., Hu, B., Yasuda, K. and Michel, A. N., “Qualitative Analysis of Discrete-Time Switched Systems,” In: Proceedings of the American Control Conference (2002) pp. 1880–1885.Google Scholar
Dixon, W. E., de Queiroz, M. S., Zhang, F. and Dawson, D. M., “Tracking control of robot manipulators with bounded torque inputs,” Robotica 17(2), 121–129 (1999).CrossRefGoogle Scholar