Tracking control and neural network disturbance observer for a 6 DoF underwater welding robot

Abstract

This article addresses the control of an underwater welding robot in 3D space. A control approach combining a finite time sliding mode controller for position and orientation control and feedback linearization for controlling one degree of freedom of the welding arm is adopted. The utilization of underwater robots in marine environments is often hindered by uncertainties and disturbances induced by ocean waves and currents, resulting in decreased accuracy and operational disruptions. To overcome these challenges, a novel observer based on a radial basis function neural network is developed to enhance the performance of the underwater welding robot. The neural network’s weights are optimized using the Lyapunov method within the control law framework. Through simulations, the article evaluates the observer’s efficacy in accurately tracking reference trajectories in the presence of uncertainties. The results underscore the significant contribution of this estimator in mitigating uncertainties and disturbances, thereby substantially improving the overall performance and operational reliability of underwater welding robots. The control strategies and observer design presented in this study pave the way for enhanced accuracy, stability, and efficiency in complex underwater welding operations.

Keywords

underwater robot welding robot finite time sliding mode observer radial basis function neural network

Get full access to this article

View all access options for this article.

References

Blomberg

AEA

Sæbø

Hansen

, et al. (2017) Automatic detection of marine gas seeps using an interferometric sidescan sonar. IEEE Journal of Oceanic Engineering 42(3): 590–602.

Cui

Zhao

, et al. (2019) Neural network-based adaptive finite-time consensus tracking control for multiple autonomous underwater vehicles. IEEE Access 7: 33064–33074.

Pan

(2009) Control of Ships and Underwater Vehicles: Design for Underactuated and Nonlinear Marine Systems. Berlin, Germany: Springer Science & Business Media.

Elmokadem

Zribi

Youcef-Toumi

(2017) Terminal sliding mode control for the trajectory tracking of underactuated autonomous underwater vehicles. Ocean Engineering 129: 613–625.

Ersoy

(2020) Recent trends and development of underwater welding. International Journal of Engineering Science and Application 4(1): 36–44.

Field

Cherchas

Calisal

(2000) Optimal control of an autonomous underwater vehicle. In: World automatic congress, Farmingdale, NY, 04 May 2012.

Hassanein

Anavatti

Shim

, et al. (2016) Model-based adaptive control system for autonomous underwater vehicles. Ocean Engineering 127: 58–69.

R-H

K-L

Hua

, et al. (2010) The application of fuzzy control in underwater welding seam-tracking system. In: 2010 seventh international conference on fuzzy systems and knowledge discovery, Yantai, China, 10–12 August 2010. IEEE.

Jawhar

Mohamed

Al-Jaroodi

, et al. (2019) An architecture for using autonomous underwater vehicles in wireless sensor networks for underwater pipeline monitoring. IEEE Transactions on Industrial Informatics 15(3): 1329–1340.

10.

Keymasi Khalaji

Bahrami

(2022) Finite-time sliding mode control of underwater vehicles in 3D space. Transactions of the Institute of Measurement and Control 44(16): 3215–3228.

11.

Keymasi Khalaji

Ghane

(2022) A physically motivated control algorithm for an autonomous underwater vehicle. Proceedings of the Institution of Mechanical Engineers - Part C: Journal of Mechanical Engineering Science 236(2): 1234–1243.

12.

Kumar

Rastogi

Gupta

(2017) A hybrid impedance control scheme for underwater welding robots with a passive foundation in the controller domain. Simulation 93(7): 619–630.

13.

Łabanowski

Fydrych

Rogalski

(2008) Underwater welding–a review. Advances in Materials Science 8(10): 2478.

14.

J-H

Lee

P-M

(2005) Design of an adaptive nonlinear controller for depth control of an autonomous underwater vehicle. Ocean Engineering 32(17–18): 2165–2181.

15.

Luo

Tao

Hao

, et al. (2019) Centroid variability model–based control of HITUWV for automatic underwater welding with enhanced stability and accuracy. Advances in Mechanical Engineering 11(11): 168781401989021.

16.

Mirzaeinejad

(2018) Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network. Applied Soft Computing 70: 318–329.

17.

Salgado-Jimenez

Jouvencel

(2003) Using high-order sliding modes for diving control a torpedo autonomous underwater vehicle. In: Oceans 2003. Celebrating the past in teaming toward the future. IEEE Cat. No. 03CH37492), San Diego, CA, 22–26 September 2003.

18.

Salgado-Jimenez

Spiewak

Fraisse

, et al. (2004) A robust control algorithm for AUV: based on a high order sliding mode. In: Oceans’ 04 MTS/IEEE Techno-Ocean’04 (IEEE Cat. No. 04CH37600), Kobe, Japan, 9–12 November 2004.

19.

Song

Smith

(2000) Automatic design and optimization of fuzzy logic controllers for an autonomous underwater vehicle. In: OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No. 00CH37158), Providence, RI, 11–14 September 2000. IEEE.

20.

Soylu

Buckham

Podhorodeski

(2008) A chattering-free sliding-mode controller for underwater vehicles with fault-tolerant infinity-norm thrust allocation. Ocean Engineering 35(16): 1647–1659.

21.

Verma

Garg

(2012) Underwater welding-recent trends and future scope. International Journal on Emerging Technologies 3(1): 115–120.

22.

Tang

Han

, et al. (2019) Robust finite-time attitude tracking control of a CMG-based AUV with unknown disturbances and input saturation. IEEE Access 7: 56409–56422.

23.

Xiang

Lapierre

, et al. (2018) Robust magnetic tracking of subsea cable by AUV in the presence of sensor noise and ocean currents. IEEE Journal of Oceanic Engineering 43(2): 311–322.

24.

Zhang

Xiao

Sun

(2006) Research on maneuverability and simulation of underwater vehicles for pipeline detection and maintenance. China Ocean Engineering 20(1): 147–154.