SYSTEM IDENTIFICATION (SI) MODELLING, CONTROLLER DESIGN AND HARDWARE TESTING FOR VERTICAL TRAJECTORY OF UNDERWATER REMOTELY OPERATED VEHICLE (ROV)

Authors

DOI:

https://doi.org/10.31436/iiumej.v24i2.2759

Keywords:

System Identification (SI) Modelling, Remotely operated vehicle (ROV), depth control, PID controller

Abstract

Underwater remotely operated vehicles (ROV) are important in marine industries to accomplish underwater exploration and surveying. The underwater environment makes it hard for ROV operators to control the manipulator while holding position simultaneously. This led to modelling and controller design for the vertical trajectory of ROV. In this paper, the System Identification (SI) modeling technique was used to model the vertical trajectory of the ROV. Then, the Proportional, Integral, and Derivative (PID) controller was implemented to control the trajectory. The SI modelling technique was used as it estimates the model based on the input and output relationship. MATLAB SI toolbox was used as the analytical software. Step and multiple step inputs were given to the system and the responses were recorded. The model with the best fit of 84.7% was selected and verified by comparing with actual output. The model response was then analyzed and the PID controller was implemented. The actual model had high percent overshoot (%OS) and steady state error (SSE). The PID implementation successfully reduced the %OS and eliminated the SSE.

ABSTRAK: Kenderaan bawah air kendalian jauh (ROV) adalah penting dalam industri marin bagi melaksanakan penerokaan dan pemerhatian bawah air. Persekitaran dalam air menyukarkan pengendali ROV bagi memanipulasi manipulator sambil memastikan kedudukan ROV secara serentak. Ini membawa kepada pemodelan dan mereka bentuk kawalan pergerakan menegak bagi ROV. Kajian ini menggunakan teknik pemodelan Sistem Pengenalan (SI) bagi memodelkan pergerakan menegak ROV. Kemudian, kawalan seimbang, menyeluruh dan terbitan (PID) dilaksanakan bagi mengawal trajektori. Teknik pemodelan SI digunakan kerana ia menganggarkan model berdasarkan kemasukan dan keluaran. Aplikasi MATLAB SI digunakan sebagai perisian analisis. Masukan satu langkah dan berbilang kali masukan telah dijalankan dan respons sistem direkodkan. Model yang paling sesuai mencapai 84.7% dipilih dan disahkan dengan perbandingan nilai keluaran sebenar. Respons model kemudiannya dianalisis dan kawalan PID dilaksanakan. Model sebenar mempunyai peratusan tinggi melampaui (%OS) dan ralat keadaan stabil (SSE). Pelaksanaan PID telah berjaya mengurangkan %OS dan menghapuskan SSE.

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References

Li G, Wu J, Tang T, Chen Z, Chen J, Liu H. (2019) Underwater acoustic time delay estimation based on envelope differences of correlation functions. Sensors, 19(5): 1-11.

doi: 10.3390/s19051259. DOI: https://doi.org/10.3390/s19051259

Binugroho EH, Wafiqqurochman, Mas’Udi MI, Setyawan B, Dewanto RS, and D. Pramadihanto D. (2019) EROV: Depth and Balance Control for ROV Motion using Fuzzy PID Method. IES 2019 - Int. Electron. Symp. Role Techno-Intelligence Creat. an Open Energy Syst. Towar. Energy Democr. Proc., pp. 637-643. doi: 10.1109/ELECSYM.2019.8901673. DOI: https://doi.org/10.1109/ELECSYM.2019.8901673

Goheen KR, Jefferys ER. (1990) Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles. IEEE J. Ocean. Eng., 15(3): 144-151.

doi: 10.1109/48.107142. DOI: https://doi.org/10.1109/48.107142

Aras MSM, Abdullah SS. (2015) Adaptive simplified fuzzy logic controller for depth control of underwater remotely operated vehicle. Indian J. Geo-Marine Sci., 44(12): 1995-2007.

B. Huang and Q. Yang, “Double-loop sliding mode controller with a novel switching term for the trajectory tracking of work-class ROVs,” Ocean Eng., vol. 178, no. March, pp. 80–94, 2019, doi: 10.1016/j.oceaneng.2019.02.043. DOI: https://doi.org/10.1016/j.oceaneng.2019.02.043

N. Kumar and M. Rani, “An efficient hybrid approach for trajectory tracking control of autonomous underwater vehicles,” Appl. Ocean Res., vol. 95, no. October 2019, p. 102053, 2020, doi: 10.1016/j.apor.2020.102053. DOI: https://doi.org/10.1016/j.apor.2020.102053

G. Antonelli, S. Chiaverini, N. Sarkar, and M. West, “Adaptive control of an autonomous underwater vehicle: Experimental results on ODIN,” IEEE Trans. Control Syst. Technol., vol. 9, no. 5, pp. 756–765, 2001, doi: 10.1109/87.944470. DOI: https://doi.org/10.1109/87.944470

M. C. Nielsen, M. Blanke, and I. Schjølberg, “Efficient Modelling Methodology for Reconfigurable Underwater Robots,” IFAC-PapersOnLine, vol. 49, no. 23, pp. 74–80, 2016, doi: 10.1016/j.ifacol.2016.10.324. DOI: https://doi.org/10.1016/j.ifacol.2016.10.324

M. S. M. Aras, S. N. B. S. Salim, E. C. S. Hoo, I. A. B. W. A. Razak, and M. H. Bin Hairi, “Comparison of fuzzy control rules using MATLAB toolbox and simulink for DC induction motor-speed control,” SoCPaR 2009 - Soft Comput. Pattern Recognitpp. 711–715, 2009, doi: 10.1109/SoCPaR.2009.143. DOI: https://doi.org/10.1109/SoCPaR.2009.143

Z. Tang, Luojun, and Q. He, “A fuzzy-PID depth control method with ., overshoot suppression for underwater vehicle,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6329 LNCS, no. PART 2, pp. 218–224, 2010, doi: 10.1007/978-3-642-15597-0_24. DOI: https://doi.org/10.1007/978-3-642-15597-0_24

M. S. Bin Mohd Aras, S. M. S. B. Syed Abdul Hamid, F. B. A. Azis, F. A. B. Ali, and S. Shah B Abdullah, “Study of the effect in the output membership function when tuning a Fuzzy logic controller,” Proc. - 2011 IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2011, pp. 1–6, 2011, doi: 10.1109/ICCSCE.2011.6190485. DOI: https://doi.org/10.1109/ICCSCE.2011.6190485

M. L. Corradini and G. Orlando, “Brief Papers,” Brain Cogn., vol. 32, no. 2, pp. 273–344, 1996, doi: 10.1006/brcg.1996.0066. DOI: https://doi.org/10.1006/brcg.1996.0066

M. . Aras, M.S.M., Abdullah, S.S. , Baharin, K.A. Nor, A.S.B.M. ,Mohd Zambri, “Model identification of an underwater remotely operated vehicle using system identification approach based on NNPC,” Int. Rev. Autom. Control, vol. 8, no. 2, pp. 149–154, 2015. DOI: https://doi.org/10.15866/ireaco.v8i2.5525

J. Ko, N. Takata, K. Thu, and T. Miyazaki, “Dynamic modeling and validation of a carbon dioxide heat pump system,” Evergreen, vol. 7, no. 2, pp. 172–194, 2020, doi: 10.5109/4055215. DOI: https://doi.org/10.5109/4055215

K. Ishaque, S. S. Abdullah, S. M. Ayob, and Z. Salam, “Single input fuzzy logic controller for unmanned underwater vehicle,” J. Intell. Robot. Syst. Theory Appl., vol. 59, no. 1, pp. 87–100, 2010, doi: 10.1007/s10846-010-9395-x. DOI: https://doi.org/10.1007/s10846-010-9395-x

M. W. N. Azmi et al., “Comparison of controllers design performance for underwater remotely operated vehicle (ROV) depth control,” Int. J. Eng. Technol., vol. 7, no. 3.14 Special Issue 14, pp. 419–423, 2018.

N. Weake, M. Pant, A. Sheoran, A. Haleem, and H. Kumar, “Optimising parameters of fused filament fabrication process to achieve optimum tensile strength using artificial neural network,” Evergreen, vol. 7, no. 3, pp. 373–381, 2020. DOI: https://doi.org/10.5109/4068614

H. Han, M. Hatta, and H. Rahman, “Smart ventilation for energy conservation in buildings,” Evergreen, vol. 6, no. 1, pp. 44–51, 2019, doi: 10.5109/2321005. DOI: https://doi.org/10.5109/2321005

H. Yu, C. Guo, and Z. Yan, “Globally finite-time stable three-dimensional trajectory-tracking control of underactuated UUVs,” Ocean Eng., vol. 189, no. March, p. 106329, 2019, doi: 10.1016/j.oceaneng.2019.106329. DOI: https://doi.org/10.1016/j.oceaneng.2019.106329

F. N. Zohedi, M. S. Mohd Aras, H. A. Kasdirin, and M. B. Bahar, “A new tuning approach of Single Input Fuzzy Logic Controller (SIFLC) for Remotely Operated Vehicle (ROV) depth control,” Evergreen, vol. 8, no. 3, pp. 651–657, 2021, doi: 10.5109/4491657. DOI: https://doi.org/10.5109/4491657

T. N. Dief and S. Yoshida, “System identification for Quad-rotor parameters using neural network,” Evergreen, vol. 3, no. 1, pp. 6–11, 2016, doi: 10.5109/1657380. DOI: https://doi.org/10.5109/1657380

T. N. Dief and S. Yoshida, “System identification and adaptive control of mass-varying quad-rotor,” Evergreen, vol. 4, no. 1, pp. 58–66, 2017, doi: 10.5109/1808454. DOI: https://doi.org/10.5109/1808454

M. Sanap, S. Chaudhari, C. Vartak, and P. Chimurkar, “HYDROBOT: An underwater surveillance swimming robot,” Proc. - 2018 Int. Conf. Commun. Inf. Comput. Technol. ICCICT 2018, vol. 2018-Janua, pp. 1–7, 2018, doi: 10.1109/ICCICT.2018.8325872. DOI: https://doi.org/10.1109/ICCICT.2018.8325872

M. S. M. Aras, S. S. Abdullah, M. Z. A. Rashid, A. A. Rahman, and M. A. A. Aziz, “Development and modeling of unmanned underwater remotely operated vehicle using system identification for depth control,” J. Theor. Appl. Inf. Technol., vol. 56, no. 1, pp. 136–145, 2013.

M. H. F. Taib, M. N., Adnan, R. and Rahiman, Practical System Identification. Penerbit UiTM, 2007.

M. A. Salim, A. Noordin, and A. N. Jahari, “A robust of fuzzy logic and proportional derivative control system for monitoring underwater vehicles,” 2nd Int. Conf. Comput. Res. Dev. ICCRD 2010, pp. 849–853, 2010, doi: 10.1109/ICCRD.2010.187. DOI: https://doi.org/10.1109/ICCRD.2010.187

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Published

2023-07-04

How to Cite

Zohedi, F. N., MOHD ARAS, M. S., Kasdirin, H. A., Bahar, M. B., & Abdullah, L. (2023). SYSTEM IDENTIFICATION (SI) MODELLING, CONTROLLER DESIGN AND HARDWARE TESTING FOR VERTICAL TRAJECTORY OF UNDERWATER REMOTELY OPERATED VEHICLE (ROV). IIUM Engineering Journal, 24(2), 131–140. https://doi.org/10.31436/iiumej.v24i2.2759

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Electrical, Computer and Communications Engineering

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