Event-triggered robust formation control of multi quadrotors for transmission line inspection

Tua Agustinus Tamba; Benedictus Christo Geroda Cinun; Yul Yunazwin Nazaruddin; Muhammad Zakiyullah Romdlony; Bin Hu; Tua Agustinus Tamba; Benedictus Christo Geroda Cinun; Yul Yunazwin Nazaruddin; Muhammad Zakiyullah Romdlony; Bin Hu

doi:10.55981/j.mev.2024.1106

Event-triggered robust formation control of multi quadrotors for transmission line inspection

Tua Agustinus Tamba, Benedictus Christo Geroda Cinun, Yul Yunazwin Nazaruddin, Muhammad Zakiyullah Romdlony, Bin Hu

Abstract

This paper proposes an event-triggered formation control scheme to manage the operation of multiple quadrotors in performing the inspection of a power transmission line. In particular, the problem of controlling such multi quadrotors to track the tower and/or cables of the transmission lines is considered. A multi-agent sliding mode control method is used for this purpose and is equipped with both a radial basis function neural network as an estimator of environmental wind disturbances, as well as an event-triggered scheduling scheme for the control execution framework. The proposed multi quadrotors control method is designed by considering the transmission tower/cable as the reference sliding surface. Simulation results are presented to illustrate the effectiveness of the proposed multi quadrotors control scheme when implemented in a case scenario of tracking the commonly-encountered shape of transmission cables. Simulation results are presented and show how the implementation of a position error-based event-triggered control enables all UAVs to track the desired position and maintain a pre-determined formation. In particular, all UAVs can minimize the tracking error within 0.05 m after reaching the desired positions since the control signal is updated if the error reaches such an error bound.

10.55981/j.mev.2024.1106

Keywords

event-triggered control; formation control; multi-agent systems; radial basis function; sliding mode control.

Full Text:

PDF

References

PT. PLN Persero, “Statistics PLN 2023,” 2024. https://web.pln.co.id/statics/uploads/2024/08/Bk-Statistik-PLN-2023-English-31.7.24.pdf

A. B. Alhassan, X. Zhang, H. Shen, and H. Xu, “Power transmission line inspection robots: A review, trends and challenges for future research,” Int. J. Electr. Power & Energy Syst., vol. 118, p. 105862, 2020, doi: https://doi.org/10.1016/j.ijepes.2020.105862.

Y. Luo, X. Yu, D. Yang, and B. Zhou, “A survey of intelligent transmission line inspection based on unmanned aerial vehicle,” Artif. Intell. Rev., vol. 56, no. 1, pp. 173–201, 2023, doi: https://doi.org/10.1007/s10462-022-10189-2.

Z. Wang, Q. Gao, J. Xu, and D. Li, “A review of UAV power line inspection,” in Advances in Guidance, Navigation and Control: Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020, Tianjin, China, October 23--25, 2020, 2022, pp. 3147–3159, doi: https://doi.org/10.1007/978-981-15-8155-7_263.

C. Xu, Q. Li, Q. Zhou, S. Zhang, D. Yu, and Y. Ma, “Power line-guided automatic electric transmission line inspection system,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–18, 2022.

X. Li, Z. Li, H. Wang, and W. Li, “Unmanned aerial vehicle for transmission line inspection: status, standardization, and perspectives,” Front. Energy Res., vol. 9, p. 713634, 2021, doi: https://doi.org/10.3389/fenrg.2021.713634.

M. Chen et al., “Environment perception technologies for power transmission line inspection robots,” J. Sensors, vol. 2021, no. 1, p. 5559231, 2021, doi: https://doi.org/10.1155/2021/5559231.

S. Kim, D. Kim, S. Jeong, J.-W. Ham, J.-K. Lee, and K.-Y. Oh, “Fault diagnosis of power transmission lines using a UAV-mounted smart inspection system,” IEEE access, vol. 8, pp. 149999–150009, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3016213.

M. D. F. Ahmed, J. C. Mohanta, and A. Sanyal, “Inspection and identification of transmission line insulator breakdown based on deep learning using aerial images,” Electr. Power Syst. Res., vol. 211, p. 108199, 2022, doi: https://doi.org/10.1016/j.epsr.2022.108199.

H. A. Foudeh, P. C.-K. Luk, and J. F. Whidborne, “An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: A comprehensive review,” IEEE Access, vol. 9, pp. 130410–130433, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3110159.

D.-Q. Chen, X.-H. Guo, P. Huang, and F.-H. Li, “Safety distance analysis of 500kv transmission line tower uav patrol inspection,” IEEE Lett. Electromagn. Compat. Pract. Appl., vol. 2, no. 4, pp. 124–128, 2020, doi: https://doi.org/10.1109/LEMCPA.2020.3040878.

X. Yue, H. Wang, and Y. Jiang, “A novel 110 kV power line inspection robot and its climbing ability analysis,” Int. J. Adv. Robot. Syst., vol. 14, no. 3, p. 1729881417710461, 2017, doi: https://doi.org/10.1177/1729881417710461.

L. Yang, J. Fan, Y. Liu, E. Li, J. Peng, and Z. Liang, “A review on state-of-the-art power line inspection techniques,” IEEE Trans. Instrum. Meas., vol. 69, no. 12, pp. 9350–9365, 2020, doi: https://doi.org/10.1109/TIM.2020.3031194.

Y. Zhang, X. Yuan, Y. Fang, and S. Chen, “UAV low altitude photogrammetry for power line inspection,” ISPRS Int. J. GEO-information, vol. 6, no. 1, p. 14, 2017, doi: https://doi.org/10.3390/ijgi6010014.

H. Guan et al., “UAV-lidar aids automatic intelligent powerline inspection,” Int. J. Electr. Power & Energy Syst., vol. 130, p. 106987, 2021, doi: https://doi.org/10.1016/j.ijepes.2021.106987.

D. J. Almakhles, “Robust backstepping sliding mode control for a quadrotor trajectory tracking application,” IEEE access, vol. 8, pp. 5515–5525, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2962722.

T. Madani and A. Benallegue, “Backstepping sliding mode control applied to a miniature quadrotor flying robot,” in IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, 2006, pp. 700–705, doi: https://doi.org/10.1109/IECON.2006.347236.

Z. Jia, J. Yu, Y. Mei, Y. Chen, Y. Shen, and X. Ai, “Integral backstepping sliding mode control for quadrotor helicopter under external uncertain disturbances,” Aerosp. Sci. Technol., vol. 68, pp. 299–307, 2017.

L. Ding, Q.-L. Han, X. Ge, and X.-M. Zhang, “An overview of recent advances in event-triggered consensus of multiagent systems,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1110–1123, 2017, doi: https://doi.org/10.1109/TCYB.2017.2771560.

D. V Dimarogonas, E. Frazzoli, and K. H. Johansson, “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Automat. Contr., vol. 57, no. 5, pp. 1291–1297, 2011, doi: https://doi.org/10.1109/TAC.2011.2174666.

P. Tabuada, “Event-triggered real-time scheduling of stabilizing control tasks,” IEEE Trans. Automat. Contr., vol. 52, no. 9, pp. 1680–1685, 2007, doi: https://doi.org/10.1109/TAC.2007.904277.

X. Shen, J. Gao, and P. X. Liu, “Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems,” Sensors, vol. 23, no. 7, p. 3477, 2023, doi: https://doi.org/10.3390/s23073477.

D.-N. Bui and M. D. Phung, “Radial basis function neural networks for formation control of unmanned aerial vehicles,” Robotica, pp. 1–19, 2024, doi: https://doi.org/10.48550/arXiv.2404.13583.

F. Furrer, M. Burri, M. Achtelik, and R. Siegwart, “Rotors—a modular gazebo mav simulator framework,” Robot Oper. Syst. Complet. Ref. (Volume 1), pp. 595–625, 2016, doi: https://doi.org/10.1007/978-3-319-26054-9_23.

T. M. Inc, “MATLAB version: 9.13. 0 (R2022b),” Natick, Massachusetts, United States MathWorks Inc, 2022.