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Hybrid Strategy-based Coordinate Controller for an Underwater Vehicle Manipulator System Using Nonlinear Disturbance Observer

Published online by Cambridge University Press:  12 March 2019

Jiyong Li
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China E-mails: lijiyong@hrbeu.edu.cn,wanlei@hrbeu.edu.cn, Zhouzexing17@163.com, xuyangz@163.com
Hai Huang*
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China E-mails: lijiyong@hrbeu.edu.cn,wanlei@hrbeu.edu.cn, Zhouzexing17@163.com, xuyangz@163.com
Lei Wan
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China E-mails: lijiyong@hrbeu.edu.cn,wanlei@hrbeu.edu.cn, Zhouzexing17@163.com, xuyangz@163.com
Zexing Zhou
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China E-mails: lijiyong@hrbeu.edu.cn,wanlei@hrbeu.edu.cn, Zhouzexing17@163.com, xuyangz@163.com
Yang Xu
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China E-mails: lijiyong@hrbeu.edu.cn,wanlei@hrbeu.edu.cn, Zhouzexing17@163.com, xuyangz@163.com
*
*Corresponding author. E-mail: haihus@163.com

Summary

This paper presents a hybrid strategy-based coordinate controller with a novel nonlinear disturbance observer for autonomous underwater vehicle manipulator systems (UVMSs). This method can reduce the influence from external unknown disturbances, inner coupling effects and model uncertainties by using a modified disturbance observer. Considering the natural redundancy property of the UVMS, the redundancy resolution algorithm is often utilized to give desired trajectories in the vehicle–joint space. However, because of the calibration errors, assembling errors and numerical errors, these desired trajectories may not lead the end-effector to the goal point accurately. To realize accurate motion control even when small errors exist in the planning phase, a hybrid strategy is introduced to transform the controller in the joint–vehicle space to the controller in the task space. Numerical simulations based on a UVMS have been carried out to testify the effectiveness of the proposed coordinate controller and the hybrid strategy. During the simulations, unknown disturbances are exerted upon the system. The trajectory tracking and error fixing performances are discussed in comparative analyses. The controller also maintains robust characteristics in comparison with the passivity-based controller and the proposed controller but without the disturbance observer. Experiments are also carried out to test its performance.

Type
Articles
Copyright
© Cambridge University Press 2019 

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References

Kim, T. W. and Yuh, J., “Development of a real-time control architecture for a semi-autonomous underwater vehicle for intervention missions,Control Eng. Pract. 12(12), 15211530 (2004).CrossRefGoogle Scholar
Cui, R., Yang, C., Li, Y. and Sharma, S., “Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning,IEEE Trans. Syst., Man, Cyber. Syst. 47(6), 10191029 (2017).CrossRefGoogle Scholar
Lewandowski, C., Akin, D., Dillow, B., Limparis, N., Carignan, C., Singh, H. and Sohn, R., “Development of a Deep-sea Robotic Manipulator for Autonomous Sampling and Retrieval,” 2008 IEEE/OES Autonomous Underwater Vehicles, Woods Hole, USA (2008) pp. 16.Google Scholar
Cui, Y. and Sarkar, N., “A Unified Force Control Approach to Autonomous Underwater Manipulation,” IEEE International Conference on Robotics and Automation, San Francisco, USA (2000) pp. 12631268.Google Scholar
Wang, Y., Wang, S., Wei, Q., Tan, M., Zhou, C. and Yu, J., “Development of an underwater manipulator and its free-floating autonomous operation,IEEE/ASME Trans. Mech. 21(2), 815824 (2016).CrossRefGoogle Scholar
Fernandez, J., Prats, M., Sanz, P. and Garcia, J., “Grasping for the seabed: Developing a new underwater robot arm for shallow-water intervention,IEEE Rob. Autom. Mag. 20(4), 121130 (2013).CrossRefGoogle Scholar
Khatib, O., Yeh, X., Brantner, G., Soe, B., Kim, B., Ganguly, S., Stuart, H., Wang, S., Cutkosky, M., Edsinger, A., Mullins, P., Barham, M., Voolstra, C. R., Salama, K. N., L’Hour, M. and Creuze, V., “Ocean one: A robotic avatar for oceanic discovery,IEEE Rob. Autom. Mag. 23(4), 2029 (2016).CrossRefGoogle Scholar
Cieslak, P., Ridao, P. and Giergiel, M., “Autonomous Underwater Panel Operation by GIRONA500 UVMS: A Practical Approach to Autonomous Underwater Manipulation,” IEEE International Conference on Robotics and Automation, Seattle, USA (2015) pp. 529536.Google Scholar
Liegeois, B., “Automatic supervisory control of the configuration and behavior of multibody mechanisms,IEEE Trans. Syst. Man Cyber. 7(12), 868871 (1977).Google Scholar
Podder, T. K. and Sarkar, N., “Unified dynamics-based motion planning algorithm for autonomous underwater vehicle manipulator systems (UVMS),Robotica 22(1), 117128 (2003).CrossRefGoogle Scholar
Soylu, S., Buckham, B. and Podhorodeski, R., “Redundancy resolution for underwater mobile manipulators,Ocean Eng. 37(2), 325343 (2010).CrossRefGoogle Scholar
Wang, Y., Jiang, S., Yan, F., Gu, L. and Chen, B., “A new redundancy resolution for underwater vehicle-manipulator system considering payload,Int. J. Adv. Rob. Syst. 14(5), 110 (2017).Google Scholar
Tang, Q., Liang, L., Xie, J., Li, Y. and Deng, Z., “Task-priority redundancy resolution on acceleration level for underwater vehicle-manipulator system,Int. J. Adv. Rob. Syst. 14(4), 19 (2017).Google Scholar
Antonelli, G., Caccavale, F. and Chiaverini, S., “Adaptive tracking control of underwater vehicle-manipulator systems based on the virtual decomposition approach,IEEE Trans. Rob. Autom. 20(3), 594602 (2004).CrossRefGoogle Scholar
Santhakumar, M. and Kim, J., “Indirect adaptive control of an autonomous underwater vehicle manipulator system for underwater manipulation tasks,Ocean Eng. 54(4), 233243 (2012).Google Scholar
Mohan, S. and Kim, J., “Coordinated motion control in task space of an autonomous underwater vehicle–manipulator system,Ocean Eng. 104(262), 155167 (2015).CrossRefGoogle Scholar
Han, J. and Chung, W. K., “Active use of restoring moments for motion control of an underwater vehicle-manipulator system,IEEE J. Oceanic Eng. 39(1), 100109 (2013).CrossRefGoogle Scholar
Mclain, T., Rock, S. and Lee, M., “Experiments in the coordinated control of an underwater arm/vehicle system,Auton. Rob. 3(2–3), 213232 (1996).CrossRefGoogle Scholar
Huang, H., Tang, Q., Li, H., Liang, L., Li, W. and Pang, Y., “Vehicle-manipulator system dynamic modelling and control for underwater autonomous manipulation,Multibody Syst. Dyn. 41(2), 125147 (2017).CrossRefGoogle Scholar
Schjølberg, I. and Egeland, O., “Motion Control Underwater Vehicle-manipulator Systems Using Feedback Linearization,” Proceedings of the Third Conference on Marinecraft Maneuvering and Control, Southampton, UK (1995) pp. 4557.Google Scholar
Londhe, P., Santhakumar, M., Patre, B. and Waghmare, L., “Task space control of an autonomous underwater vehicle manipulator system by robust single-input fuzzy logic control scheme,IEEE J. Oceanic Eng. 42(1), 1328 (2016).Google Scholar
Fosse, T. I., Handbook of Marine Craft Hydrodynamics and Motion Control (John Wiley & Sons, Ltd., Chichester, UK, 2011).CrossRefGoogle Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer-Verlag, London, UK, 2009).CrossRefGoogle Scholar
Antonelli, G. and Chiaverini, S., “Fuzzy redundancy resolution and motion coordination for underwater vehicle-manipulator systems,IEEE Trans. Fuzzy Syst. 11(1), 109120 (2013).CrossRefGoogle Scholar
Yoshikawa, T., “Manipulability of robotic mechanisms,Int. J. Rob. Res. 4(2), 39 (1985).CrossRefGoogle Scholar
Li, Y., Zhang, L., Wan, L. and Liang, X., “Optimization of S-surface controller for autonomous underwater vehicle with immune-genetic algorithm,J. Harbin Inst. Technol. 15(3), 404410 (2008).Google Scholar
Antonelli, G., Underwater Robots, Underwater Robots Motion and Force Control of Vehicle Manipulator Systems (Springer-Verlag, Berlin, Germany, 2006).Google Scholar
Slotine, J. and Li, W., Applied Nonlinear Control (Prentice Hall, New Jersey, USA, 1991).Google Scholar
Yatoh, T., Sagara, S. and Tamura, M., “Digital type disturbance compensation control of a floating underwater robot with 2 link manipulator,Artif. Life Rob. 13(1), 377381 (2008).CrossRefGoogle Scholar
Chen, W., Ballance, D., Gawthrop, P. and O’Reilly, J., “A nonlinear disturbance observer for robotic manipulators,IEEE Trans. Ind. Electron. 47(4), 932938 (2000).CrossRefGoogle Scholar
Slotine, J. J. E. and Weiping, L., “Adaptive manipulator control a case study,IEEE Trans. Autom. Control 33(11), 9951003 (1988).CrossRefGoogle Scholar
Loria, A., Kelly, R. and Teel, A., “Uniform parametric convergence in the adaptive control of mechanical systems,Eur. J. Control 11(2), 87100 (2005).CrossRefGoogle Scholar