Hostname: page-component-848d4c4894-cjp7w Total loading time: 0 Render date: 2024-07-07T21:23:58.487Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory tracking control of an underwater vehicle in the presence of disturbance, measurement errors, and actuator dynamic and nonlinearity

Published online by Cambridge University Press:  26 July 2023

Mostafa Hosseini Open the ORCID record for Mostafa Hosseini [Opens in a new window] ,
Abolfazl Ranjbar Noei  and
Seyed Jalil Sadati Rostami
Mostafa Hosseini*
Affiliation:
Intelligent System and Nano Devices Research Group, Department of Control Engineering, Babol Noshirvani University of Technology, Babol, Iran
Abolfazl Ranjbar Noei
Affiliation:
Intelligent System and Nano Devices Research Group, Department of Control Engineering, Babol Noshirvani University of Technology, Babol, Iran
Seyed Jalil Sadati Rostami
Affiliation:
Intelligent System and Nano Devices Research Group, Department of Control Engineering, Babol Noshirvani University of Technology, Babol, Iran
*
Corresponding author: Mostafa Hosseini; E-mail: hosseini64@nit.ac.ir
Get access Rights & Permissions [Opens in a new window]

Abstract

Underwater vehicles are rich systems with attractive and challenging properties such as nonlinearities, external disturbances, and underactuated dynamics. These make the design of an advanced and robust controller quite a challenging task. This paper focuses on designing a model-free high-order sliding mode controller in a six-degree-of-freedom trajectory tracking task. The purpose of the control is accurate trajectory tracking and considerably reducing the chattering phenomenon in situations where the remotely operated vehicle (ROV) works in the presence of external disturbances, measurement errors, and actuator dynamics and nonlinearity, which is not seen in previous research. To demonstrate the stability of the closed-loop system, the Lyapunov theory is employed to ensure the asymptotic stability of tracking errors. A linear Kalman filter for estimating measurement errors is proposed to be used to correct positioning system outputs (speed, position, and attitude). In a hardware-in-the-loop test, the proposed controller for the ROV is tested in a real-time application, considering external disturbances, measurement errors, and actual thrusters. In addition, comparing the outcomes with the performance of the PID controller and the supper twisting controller shows the superiority of the proposed controller. Due to the existence of the measurement noise, spectrum analysis is also performed.

Keywords

remotely operated vehiclemodel-free high order sliding modeactuator dynamicmeasurement noisehardware-in-the-loop
Type
Research Article
Information
Robotica , Volume 41 , Issue 10 , October 2023 , pp. 3059 - 3078
Copyright
© The Author(s), 2023. 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

Hosseini, M.. Improvement in ROV Horizontal Plane Cruising Using Adaptive Method. In: 2016 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, Iran (2016).Google Scholar
Hosseini, M. and Seyedtabaii, S., “Robust ROV path following considering disturbance and measurement error using data fusion,” Appl. Ocean Res. 54, 67–72 (2016).CrossRefGoogle Scholar
Hosseinabadi, P. A., Abadi, A. S. S., Mekhilef, S. and Pota, H. R., “Chattering-free trajectory tracking robust predefined-time sliding mode control for a remotely operated vehicle,” J. Control. Autom. Electr. Syst. 31(5), 1177–1195 (2020).CrossRefGoogle Scholar
Ventura, U. P. and Fridman, L., “Chattering Measurement in SMC and HOSMC,” In: 2016 14th International Workshop on Variable Structure Systems (VSS), Nanjing, China (2016) pp. 108–113.CrossRefGoogle Scholar
Levant, A., “Sliding order and sliding accuracy in sliding mode control,” Int. J. Control 58(6), 1247–1263 (1993).CrossRefGoogle Scholar
Javed, M. U., Liu, H., Nie, J. and Sun, J., “Robust tracking control for robotic manipulators based on super-twisting algorithm,” IOP Conf. Ser.: Mater. Sci. Eng. 576(1), 12017 (2019).CrossRefGoogle Scholar
Manzanilla, A., Ibarra, E., Salazar, S., Zamora, Á.E., Lozano, R. and Muñoz, F., “Super-twisting integral sliding mode control for trajectory tracking of an unmanned underwater vehicle,” Ocean Eng. 234, 109164 (2021).CrossRefGoogle Scholar
Sverdrup-Thygeson, J., Kelasidi, E., Pettersen, K. Y. and Gravdahl, J. T., “Modeling of underwater swimming manipulators,” IFAC-PapersOnLine 49(23), 81–88 (2016).CrossRefGoogle Scholar
Shojaei, K. and Chatraei, A., “Robust platoon control of underactuated autonomous underwater vehicles subjected to nonlinearities, uncertainties and range and angle constraints,” Appl. Ocean Res. 110, 102594 (2021).CrossRefGoogle Scholar
Liang, H., Fu, Y., Gao, J. and Cao, H., “Finite-time velocity-observed based adaptive output-feedback trajectory tracking formation control for underactuated unmanned underwater vehicles with prescribed transient performance,” Ocean Eng. 233, 109071 (2021).CrossRefGoogle Scholar
Xu, J., Wang, M. and Qiao, L., “Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles,” Ocean Eng. 105, 54–63 (2015).CrossRefGoogle Scholar
Karkoub, M., Wu, H.-M. and Hwang, C.-L., “Nonlinear trajectory-tracking control of an autonomous underwater vehicle,” Ocean Eng. 145, 188–198 (2017).CrossRefGoogle Scholar
Long, C., Qin, X., Bian, Y. and Hu, M., “Trajectory tracking control of ROVs considering external disturbances and measurement noises using ESKF-based MPC,” Ocean Eng. 241, 109991 (2021). doi: 10.1016/j.oceaneng.2021.109991.CrossRefGoogle Scholar
Qiao, L. and Zhang, W., “Adaptive second-order fast nonsingular terminal sliding mode tracking control for fully actuated autonomous underwater vehicles,” IEEE J. Ocean. Eng. 44(2), 363–385 (2018).CrossRefGoogle Scholar
Huang, B. and Yang, Q., “Disturbance observer-based double-loop sliding-mode control for trajectory tracking of work-class ROVs,” J. Mar. Sci. Eng. 10(5), 601 (2022).CrossRefGoogle Scholar
Li, M., Yu, C., Zhang, X., Liu, C. and Lian, L., “Fuzzy adaptive trajectory tracking control of work-class ROVs considering thruster dynamics,” Ocean Eng. 267, 113232 (2023). doi: 10.1016/j.oceaneng.2022.113232.CrossRefGoogle Scholar
Hoang, N. Q. and Kreuzer, E., “A robust adaptive sliding mode controller for remotely operated vehicles,” Tech. Mech. Sci. J. Fundam. Appl. Eng. Mech. 28(3-4), 185–193 (2008).Google Scholar
Salgado-Jiménez, T., García-Valdovinos, L. G., Delgado-Ramírez, G. and Bartoszewicz, A., “Control of ROVs using a model-free 2nd-order sliding mode approach,” In: Sliding Mode Control, (BOB—Books on Demand: Norderstedt, Germany, 2011) pp. 347–368.Google Scholar
Koh, T. H., Lau, M. W. S., Low, E., Seet, G., Swei, S. and Cheng, P. L., “A study of the control of an underactuated underwater robotic vehicle,” IEEE/RSJ Int. Conf. Intell. Robot. Syst. 2, 2049–2054 (2002).Google Scholar
Wan, L. and Wang, F., “Modeling and motion control strategy for AUV,” In: Autonomous Underwater Vehicles (N. A. Cruz, ed), (InTech, Rijeka, Croatia, 2011) pp. 133–146.Google Scholar
Aras, M. S. M., Abdullah, S. S., Rahman, A. A. and Aziz, M. A. A., “Thruster modelling for underwater vehicle using system identification method,” Int. J. Adv. Robot. Syst. 10(5), 252 (2013).CrossRefGoogle Scholar
Parra-Vega, V., Arimoto, S., Liu, Y.-H., Hirzinger, G. and Akella, P., “Dynamic sliding PID control for tracking of robot manipulators: Theory and experiments,” IEEE Trans. Robot. Autom. 19(6), 967–976 (2003).CrossRefGoogle Scholar
Parra-Vega, V. and Arimoto, S.. Adaptive control for robot manipulators with sliding mode error coordinate system: Free and constrained motions, Proc. 1995 IEEE Int. Conf. Robot. Autom., 1, (1995) pp. 591–596.CrossRefGoogle Scholar
García-Valdovinos, L. G., Salgado-Jiménez, T., Bandala-Sánchez, M., Nava-Balanzar, L., Hernández-Alvarado, R. and Cruz-Ledesma, J. A., “Modelling, design and robust control of a remotely operated underwater vehicle,” Int. J. Adv. Robot. Syst. 11(1), 1 (2014).CrossRefGoogle Scholar
Eker, I., “Second-order sliding mode control with experimental application,” ISA Trans. 49(3), 394–405 (2010).CrossRefGoogle ScholarPubMed
Fang, J.-S., Tsai, J. S.-H., Yan, J.-J. and Guo, S.-M., “Adaptive chattering-free sliding mode control of chaotic systems with unknown input nonlinearity via smooth hyperbolic tangent function,” Math. Probl. Eng. 2019, 1–9 (2019).Google Scholar
Dinc, M. and Hajiyev, C., “Integration of navigation systems for autonomous underwater vehicles,” J. Mar. Eng. Technol. 14(1), 32–43 (2015).CrossRefGoogle Scholar
Sokolović, V., Dikic, G., Markovic, G., Stancic, R. and Lukic, N., “INS/GPS navigation system based on MEMS technologies,” Strojniški Vestnik-J. Mech. Eng. 61(7-8), 448–458 (2015).CrossRefGoogle Scholar
Lei, H. R., Chen, S., Chang, Y. W. and Wang, L. J., “Research on the hardware-in-the-loop simulation for high dynamic SINS/GNSS integrated navigation system,” Adv. Mater. Res. 846, 378–382 (2014).Google Scholar
Adewusi, S., “Modeling and parameter identification of a DC motor using constraint optimization technique,” IOSR J. Mech. Civ. Eng. 13, 45–56 (2016).Google Scholar
Proaño, P., Capito, L., Rosales, A. and Camacho, O.. Sliding Mode Control: Implementation Like Pid for Trajectory-Tracking for Mobile Robots. In: 2015 Asia-Pacific Conference on Computer Aided System Engineering, Quito, Ecuador (2015) pp. 220–225.Google Scholar