Abstract
This note investigates the dynamic positioning control problem of full-actuated marine vehicles in the presence of system uncertainties and input constraints. In the control algorithm, the ship model uncertainty is approximated by the radial-basis-function neural networks. The effect of input saturation is analyzed by the auxiliary system, states of the auxiliary system are employed to develop the control scheme. By virtue of the dynamic surface control technique, the inherent problem of “explosion of complexity” problem occurred during conventional Backstepping design framework is avoided. The semi-global uniformly ultimately bounded stability of the closed-loop is guaranteed by Lyapunov theory. Since the minimal learning parameterization based adaptive law is derived, the number of parameters updated online is reduced to 6. Consider the servo-system uncertainty, the control inputs of interest are achieved as the measurable propeller pitch whose characteristics would facilitate the implementation of the algorithm in the practical engineering. Finally, by employing a supply vessel as the plant, comparison simulations are conducted to demonstrate the effectiveness and robustness of the proposed algorithm.
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References
Sørensen Asgeir J (2011) A survey of dynamic positioning control systems. Annu Rev Control 35:123–126
Grøvlen A, Fossen TI (1996) Nonlinear control of dynamic positioned ships using only position feedback: an observer backstepping approach. In: Proceedings of the 35th IEEE conference on decision and control, Kobe, Japan, pp 3388–3393
Do KD (2011) Global robust and adaptive output feedback dynamic positioning of surface ships. J Mar Sci Appl 10:325–332
Fossen TI, Grøvlen A (1997) Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping. IEEE Trans Control Syst Technol 6(1):121–128
Fossen TI, Strand JP (1999) Passive nonlinear observer design for ships using lyapunov methods: full-scal experiment with a supply vessel. Automatica 35(1):3–16
Quek ST, Nguyen TD, Sørensen Asgeir J (2007) Design of hybrid controller for dynamic positioning from calm to extreme sea condition. Automatica 43:765–785
Du J, Hu X, Liu H, Chen CL Philip (2015) Adaptive robust output feedback control for a marine dynamic positioning system based on a high-gain observer. IEEE Trans Neural Netw Learn Syst 26(11):1775–1786
Gao J, Proctor AlA, Shi Y, Bradley C (2016) Hierarchical model predictive image-based visual servoing of underwater vehicles with adaptive neural network dynamic control. IEEE Trans Cybern 46(10):2323–2334
Zhang G, Zhang X, Zheng Y (2015) Adaptive neural path-following control for underactuated ships in fields of marine practice. Ocean Eng 104:558–567
Pan J, Do KD, Jiang ZP (2002) Underactuated ship global tracking under relaxed conditions. IEEE Trans Autom Control 47(9):1529–1536
Ma B, Xie W (2015) Robust global uniform asymptotic stabilization of underactuated surface vessels with unknown model parameters. IEEE Trans Autom Control 25:1037–1050
Cui R, Yang C, Li Y, Sharma S (2014) Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation. In: Proceedings of UKACC international conference on control, Loughborough, UK, pp 50–55
Zhang JZ, Bi EY, Wei YJ (2010) Position-tracking control of underactuated autonomous underwater vehicles in the presence of unknown ocean currents. IET Control Theory Appl 4(11):2369–2380
Godoy SA, Serrano ME, Scaglia GJE (2014) Trajectory tracking of underactuated surface vessels: a linear algebra approach. IEEE Trans Control Syst Technol 22(3):1103–1111
He W, Dong Y, Sun C (2016) Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern Syst 46(3):334–344
Pascoal AM, Aguiar AP (2007) Dynamic positioning and way-point tracking of underactuated auvs in the presence of ocean currents. Int J Control 80(7):1037–1050
Guo Y, Dong W (2006) Global time-varying stabilization of underactuated surface vessel. IEEE Trans Autom Control 50(6):859–864
Ren B, Chen M, Ge SS (2011) Adaptive tracking control of uncertain mimo nonlinear systems with input constraints. Automatica 47(3):452–465
Fossen TI, Petersen KY (2000) Underactuated dynamic positioning of a ship-experimental results. IEEE Trans Control Syst Technol 8(5):863–891
Jiang M, Chen B (2013) Adaptive control and constrained control allocation for overactuated ocean surface vessels. Int J Syst Sci 44(12):2295–2309
Wang H, Wang D, Peng Z (2014) Adaptive dynamic surface control for cooperative path following of marine surface vehicles with input saturation. Nonlinear Dyn 77(1):107–117
Perez T, Donaire A (2009) Constrained control design for dynamic positioning of marine vehicles with control allocation. Automatica 30(2):57–70
Zhang Guoqing, Zhang Xianku (2013) Concise robust adaptive path-following control of underactuated ships using DSC and MLP. IEEE J Ocean Eng 39(4):685–694
Lin Y, Du J, Hu X, Chen H (2014) Design of neural network observer for ship dynamic positioning system. In: Proceedings of the 33th IEEE Chinese control conference, Nanjing, China, pp 2518–2522
Yang Y, Wang X (2007) Adaptive NN tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks. Neurocomputing 70:932–941
Fossen I Thor (2011) Handbook of marine craft hydrodynamics and motion control. Wiley, New York
Zhang X, Zhang G (2013) Researches on the williamson turn for very large carriers. Naval Eng J 4(125):113–120
Do KD, Pan J (2006) Global robust adaptive path following of underactuated ships. Automatica 42:1713–1722
Chen W-S (2009) Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks. IET Control Theory Appl 3(10):1383–1394
Huo W, Zheng Z (2013) Planar path following control for stratospheric airship. IET Control Theory Appl 7(2):185–201
Fossen TI, Sagatun SI, Sørensen AJ (1996) Identification of dynamically positioned ships. Control Eng Pract 4(3):369–376
Pettersen KY, Fredriksen E (2006) Global-exponential waypoint maneuvering of ships: theory and experiments. Automatic 42(4):677–687
Acknowledgements
This work is partially supported by the National Postdoctoral Program for Innovative Talents (Grant no. BX201600103) and the Natural Science Foundation of Liaoning Province (Grant no. 20170520189, 20180520039), the Program for Innovative Research Team in University (no. IRT17R13), the National Natural Science Foundation of China (Grant nos. 51679024), and the Fundamental Research Funds for the Central University (Grant no. 3132018301, 3132018304, 3132016315). The authors would like to thank anonymous reviewers for their valuable comments to improve the quality of this article.
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Zhang, G., Huang, C., Zhang, X. et al. Robust adaptive control for dynamic positioning ships in the presence of input constraints. J Mar Sci Technol 24, 1172–1182 (2019). https://doi.org/10.1007/s00773-018-0616-5
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DOI: https://doi.org/10.1007/s00773-018-0616-5